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Curriculum Vitae of Aurelio Piazzi

Professor of Systems and Control Engineering
University of Parma
Department of Information Engineering
Viale G.P. Usberti, 181A
43124 Parma, ITALY
Phone +39 0521 905733
Email aurelio.piazzi@unipr.it
Web http://www.ce.unipr.it/people/piazzi/

Biosketch

Aurelio Piazzi was born in Medicina (BO), Italy, on November 1957. He received the Laurea degree magna cum laude in nuclear engineering from the University of Bologna in March 1982. In this university, he attended the Ph.D. course in Systems Engineering and received the Ph.D. degree in 1987. From 1990 to 1992, he was research associate in System Theory with Professor Giovanni Marro at the Department of Electronics, Computer Science and Systems (DEIS), University of Bologna. From November 1992 to September 2001, he was associate professor of Automatic Control at the Department of Information Engineering, University of Parma, and from October 2001 he is full professor of Systems and Control Engineering at the same university. At this department, in 1995 he promoted and established the Active Control Laboratory (ACtLab) devoted to didactic and research activities. From 2007 he is the director of the Polytechnic Library of the University of Parma.
In the years from 2000 to 2006, he is scientific head of research units of various national PRIN projects partially financed by MIUR (Ministry of Education, University and Research) on topics of automation and control theory. Scientific coordinator of bilateral research programs in collaboration with CNR, ENEL, and Worgas Bruciatori, in 2002 and 2003 he has directed the European Project COOKIES within the EU cluster Eutist-IMV (Integrated Machine Vision) in collaboration with Gruppo Colussi (Perugia) for the artificial vision-based control of food industrial ovens. In 2007 and 2008, in collaboration with RFI Ferrovie dello Stato, he has been the scientific coordinator of the research project PAVISYS (Pantograph Automatic Vision-based Inspection SYStem) devoted to the diagnosis of pantographs for electric traction.
He is senior member of IEEE and member of SIAM. His main research interests are in control theory, autonomous robotics, and mechatronics systems. His recent research activities have focused on inversion-based and optimal feedforward methods, feedforward-feedback control strategies, and autonomous navigation and parking of wheeled robots and vehicles. He has published over 100 scientific papers in international journals and conference proceedings.

Education

March 1982: Laurea degree in Nuclear Engineering magna cum laude from the University of Bologna.

May 1982: Professional Engineer certificate.

July 1987: Research Doctorate (PhD) degree in Systems Engineering from the University of Bologna.

Academic Employment

July 1990 – October 1992: Research Associate in System Theory at the Department of Electronics, Computer Science and Systems (DEIS), University of Bologna.

November 1992 – September 2001: Associate Professor of Automatic Control at the Department of Information Engineering, University of Parma.

October 2001 – present: Full Professor of Control Systems at the Department of Information Engineering, University of Parma.

November 2007 – present: Director of the Polytecnic Library of the University of Parma.

Other Employment

July 1982 – October 1983: Military Service at the Antiaircraft Missiles Artillery (Tactical Control Officer).

February – March 1987: Visiting Scholar at the Department of Electrical Engineering, Gainesville, University of Florida (invited to the Gould Seminar Series).

1987 – 1988: Consultant and Teacher of Programming Languages at the Industrial Professional Centers ENAIP (Rimini, Ancona).

January 1989 – June 1990: Research Fellowship in Computer Graphics at the Advanced CAD Laboratory of the Faculty of Engineering, University of Parma (sponsored by Pizzarotti SpA and IBM Italia SpA).

Research Interests

Control and systems theory.

Feedforward-feedback control systems.

Inversion-based control systems.

Vision-based control systems.

Optimal control of processes and mechatronics systems.

Motion control of autonomous vehicles.

Mobile robotics and automation systems.

Institutional Activities and Academic Commitees

Member of the Library Committee for the Faculty of Engineering from 1994/1995 to 2001/2002, University of Parma.

Member of the Board of the Department of Information Engineering from 1994/1995 to 2000/2001, University of Parma.

Competition Committee for a position of Research Associate in Automatic Control at the Polytechnic of Milano, 1995/1996.

Board of Competition for admission to the PhD Course in Computer and Automation Engineering (XII Cycle) at the Polytechnic of Milano, January 1997.

Competition Committee for a position of Research Associate in Automatic Control at the Polytechnic of Torino, 1997/1998.

Member of the Teaching Commission for the ‘Laurea’ Courses of Information Engineering, 1998/1999, University of Parma.

Board of Competition for admission to the PhD Course in Information Engineering Technologies (XIV Cycle) at the University of Parma, February 1999.

Competition Committee for a position of Associate Professor in Automatic Control at the University of Padova, First Session of year 1999, 1999/2000.

Board of Examiners for the Doctorate Degree in "Information Engineering" at the University of Modena, February 2000.

Board of Examiners for the Doctorate Degree in "Systems Engineering" at the University of Bologna, conduct of discussions in Padova, February 2000.

Board of Competition for admission to the PhD Course in Information Engineering Technologies (XVI Cycle) at the University of Parma, December 2000.

Secretary of the Council of ‘Laurea’ Courses in Information Engineering from October 2001 to present, University of Parma.

Board of Competition for admission to the PhD Course in Information Engineering Technologies (XVII Cycle) at the University of Parma, December 2001.

Board of Examiners for the Doctorate Degree in "Systems Engineering" at the University of Bologna, conduct of discussions in Bologna, February 2002.

Board of Examiners for the Doctorate Degree in "Systems Engineering" at the University of Bologna, May 2007.

Board of Competition for admission to the PhD Course in Information Engineering Technologies (XXV Cycle) at the University of Parma, December 2009.

Board of Examiners for the Doctorate Degree in "Information Engineering" at the University of Brescia, April 2010.

Chairman of the Board of Examiners for the State Exams of Qualification for the Profession of Engineering, First Session, 2011, University of Parma.

Chairman of the Board of Examiners for the State Exams of Qualification for the Profession of Engineering, Second Session, 2011, University of Parma.

Member of the Board of the Department of Information Engineering from 2010/2011 to present, University of Parma.

Board of Examiners for the Doctorate Degree in "Information Engineering" at the University of Modena, February 2012.

Competition Committee for a position of temporary Research Associate in Control Systems and Enginnering at the Univesity of Brescia, Italy, March 2012.

Board of Examiners for the Doctorate Degree in "Information and Automation Engineering" at the University of Brescia, March 2013.

Chairman of a Committee for 2 positions as Associate Professor in Automatica (Systems and Control Engineering) at the University of Parma, August 2014.

Scientific Boards and Committees

Member of the Board of the Research Doctorate in Information Engineering Technologies from 1993/1994 to present, University of Parma.

Member of the Scientific Board of the Research Doctorate School in Engineering and Architecture from June 2011 to July 2014, University of Parma.

Member of the Scientific Board of the Italian Chapter of IEEE Control Systems Society for the Best Young Author Journal Paper Award 2012.

Member of the Scientific Board of SPS IPC Drives, Messe Frankfurt Italy from July 2014 to present.

Teaching Experience

Automatic Control, ‘Laurea’ in Electronic Engineering, Computer Engineering, Telecomunications Engineering from 1992/1993 to 2009/2010, University of Parma.

Fundamentals of Automatic Control, ‘Laurea’ in Computer Engineering and ‘Laurea’ in Electronics and Communications Engineering from 2010/2011 to present, University of Parma.

System Theory, ‘Laurea’ in Computer Engineering from 1998/1999 to 2002/2003, University of Parma.

Industrial Robotics, University ‘Diploma’ in Computer and Automation Engineering from 1994/1995 to 1997/1998, University of Parma.

Identification and Optimization, University ‘Diploma’ in Computer and Automation Engineering, 1992/1993, University of Parma.

Multivariable Systems, ‘Laurea Specialistica’ in Computer Engineering, 2003/2004, University of Parma.

Nonlinear Systems, ‘Laurea Specialistica’ and ‘Laurea Magistrale’ (from 2010) in Computer Engineering from 2003/2004 to present, University of Parma.

Dynamic Inversion for Autonomous Vehicles Motion, PhD course in Information Engineering Technologies, 2005/2006, University of Parma.

Supervision of Students

PhD Thesis in Systems Automation

Luca Consolini, “Path Following and Controlled Invariance for Nonlinear Systems”, March 2005, University of Parma.

Massimo Romano, “Motion Generation for Autonomous Wheeled Mobile Robots using Dynamic Path Inversion”, March 2005, University of Parma.

Gabriele Lini, “Optimal Control and Path Planning for Mobile Robots”, March 2012, University of Parma.

“Laurea” Thesis in Information Engineering (5–year curriculum)

20 thesis from 1994 to 2008.

“Diploma” Thesis in Computer and Automation Engineering

16 thesis from 1995 to 2005.

“Laurea Magistrale” (Master of Science) Thesis in Information Engineering

11 thesis from 2008 to present.

“Laurea” Thesis in Information Engineering (3-year curriculum)

16 thesis from 2006 to present.

Laboratories

Promoter and coordinator of the Active Control Laboratory (ACtLab) at the Department of Information Engineering, University of Parma. Since 1995, this laboratory is the main facility for didactical and research activities in automation and control systems at the University of Parma.

Professional societies and scientific affiliations

IEEE, Institute of Electrical and Electronics Engineers; affilialiations to IEEE Control Systems Society and IEEE Robotics and Automation Society:
IEEE Member since 1991
IEEE Senior Member since 2014

SIAM, Society for Industrial and Applied Mathematics:
SIAM Member since 1995

SIDRA, Italian Society of Professors and Researchers in Control Systems (Automatica):
SIDRA Member since 2007 (year of society foundation)

Conference, society and editorial activities

Organization of special session "Vehicle Motion Control Systems" in 2000 IEEE Intelligent Vehicles Symposium, Dearborn, MI USA, 3-5 October 2000.

Organization of “Advanced School Giovanni Zappa on Robustness and Optimization Techniques for High Performance Control Systems”, Centro Santa Elisabetta, Campus of the University of Parma, 17-18 November 2006.

Organization of “1st ANIPLA Meeting – Automation: the professional engineer”, Sede didattica Riccardo Barilla, Faculty of Engineering, University of Parma, 28 April 2010.

Organization of “2nd ANIPLA Meeting – Automation: the professional engineer”, Sede didattica Riccardo Barilla, Faculty of Engineering, University of Parma, 18 April 2012.

Organization of “3rd ANIPLA Meeting – Automation: the professional engineer”, Sede didattica Riccardo Barilla, Engineering Departments, University of Parma, 15 April 2014.

Chair of Session “Control Applications 5” at the 39th Conference of IEEE Industrial Electronics Society, 10-13 November 2013, Vienna (Austria).

Member of the Didactics Commission of SIDRA from 2008 to 2010.

Current reviewer of Automatica and IEEE Transaction on Control Systems Technology; over the years reviewer of IEEE Transactions on Automatic Control, European Journal of Control, Journal of Robotic Systems, International Journal of Systems Science, Journal of Dynamic Systems, Measurement and Control.

Funded Research Projects

European Research

Scientific coordinator of the European Project COOKIES (quality COntrOl of baKIng status of ovEn productS) funded by the European Commission under the 5th Framework Programme, European Take-up of essential Information Society Technologies - Integrated Machine Vision (EUTIST-IMV); Project Partners: University of Parma, Satukunta Polytecnic (Finlandia), Colussi s.p.a Perugia, ATE-Applied Technical Engineering Oy (Finlandia); January 2002 – September 2003.

National Research

Scientific coordinator of the University of Parma for the Coordinated Project "Active safety systems for automotive applications" funded by the National Research Council CNR, years 1999-2000.

Scientific coordinator for the University of Parma on "Methods of dynamic inversion for the control of uncertain systems" within the Research Project of National Interest PRIN 2000 "Techniques for robust control of uncertain systems" (national coordinator Prof. Antonio Vicino of the University of Siena), supported by the Italian Ministry of University and Research; years 2001-2002.

Scientific coordinator for the University of Parma on “Optimization of inversion-based control systems for uncertain systems” within the Research Project of National Interest PRIN 2002 “Robust and optimization techniques for the control of uncertain systems” (national coordinator Prof. Mario Milanese of the Politechnic of Torino), supported by the Italian Ministry of University and Research; years 2003-2004.

Scientific coordinator for the University of Parma on “Synthesis methods for high-performance control systems with input constraints” within the Research Project of National Interest PRIN 2004 “Robustness and Optimization Techniques for High Performance Control Systems” (national coordinator Prof. Franco Blanchini of the University of Udine), supported by the Italian Ministry of University and Research; years 2005-2006.

Scientific coordinator for the University of Parma on “High-performance control methods for constrained systems” within the Research Project of National Interest PRIN 2006 “Advanced control and identification techniques for innovative applications” (national coordinator Prof. Franco Blanchini of the University of Udine), supported by the Italian Ministry of University and Research; years 2007-2008.

Research Projects in Collaboration with Industries and Companies

Research coordinator in charge of the Convention between the University of Parma and ENEL SpA on "Study and realization of a small olfactory robot and of the corresponding olfactory dynamic landscape", year 1998.

Research coordinator in charge of the Convention between the University of Parma and ENEL SpA on “Study of shape memory materials for innovative applications of process control”, year 1999.

Research coordinator in charge of the Convention between the University of Parma and Worgas Ricerca SpA on “Modeling and control systems for high performance burners”, year 2000.

Research coordinator in charge of the Convention between the University of Parma and Rete Ferroviaria Italiana RFI, Gruppo Ferrovie dello Stato on “Automatic inspection of pantographs for electric traction by artificial vision”, year 2007-2008. This Convention has led to the realization of the system PAVISYS (Pantograph Automatic Vision-based Inspection SYStem) adopted by RFI for the monitoring of pantographs on the Italian railway network. The project results have led to the patent “Pantograph Monitoring System and Method”, patent number PCT/EP2011/066460, 21 September 2011 by S. Cagnoni, A. Piazzi, L. Ascari, M. Sacchi.

Research contributions

The main research results have regarded the following topics:
• Geometric approach to linear multivariable control theory
• Analysis and control of linear uncertain systems
• Semi-infinite optimization and applications
• Trajectory planning for robot manipulators
• Inversion-based control
• Path generation and motion control for autonomous wheeled vehicles
• Time-optimal constrained feedforward control
• Feedforward-feedback control
• Mechatronics applications
• Vision-based automation
• Velocity planning

Geometric approach to linear multivariable control theory

The main problem considered in this context has been a general regulator problem whose aim is the synthesis of a multivariable feedback controller to achieve asymptotic tracking of exogenous signals and a complete (vector) disturbance decoupling. The chosen approach to deal with this problem was the geometric approach introduced by Giuseppe Basile and Giovanni Marro in 1969 with the pioneering work “Controlled and conditioned invariants in linear system theory”, Journal of Optimization Theory and Applications 3 (1969) 305-315. The systematic use of controlled and conditioned invariance and of geometric duality between controllability and observabilty has led to results presented in [3J], [5J], [6P], [2C].
Synthesis methods that avoid eigenspaces computations, which are notoriously difficult and ill-conditioned, were reported in [1C], [1J], [4J], [1P]. These methods are based on the lattice properties of self-bounded controlled invariants and on the dual notion of self-hidden conditioned invariants. New low-order controller synthesis for disturbance rejection were proposed in [2P], [6J]. An investigation to robust regulation with standard geometric approach techniques was reported in [3P] and [7P]. The role of invariant zeros in regulation problems has been highlighted in [4P] and [3C]. A problem of perfect output tracking was solved in [11P].
The majority of research results in this topic was coauthored with the guidance and collaboration of Professor Giovanni Marro during the PhD course in System Engineering and subsequent years at the Department of Electronics, Computer Science and Systems, University of Bologna.

Analysis and control of linear uncertain systems

Uncertainty in linear systems has been mainly focused on systems affected by uncertain parameters belonging to known real intervals. An approach using decision algebra methods was devised to analyze the robust stability in a case where the system characteristic polynomial depends nonlinearly on the uncertain parameters [7J]. A study on interval analysis – which is the extension of the “standard” mathematical analysis over the arithmetic of real interval - was also pursued to devise tools for the analysis of linear systems affected by uncertain parameters. The results were exposed in [9J] and [5C]; the latter paper describes an interval algorithm to determine the stability margin of an uncertain system.
The synthesis of an optimal fixed-structure controller for scalar system was established by minimazing an H2-index subject to an H-infinity constraint that accounts for a robust closed-loop requirement [15J]. The focus of [25J] was the worst-case design of a static output feedback for a multivariable system: robust stability is guaranteed for the whole set of uncertain paramenters while minimizing a worst-case quadratic index. A robust synthesis for set-point scalar regulation using inversion-based control was presented in [17J].

Semi-infinite optimization and applications

A significant variety of design problems for control systems and also for engineering are amenable to be recasted as semi-infinite optimization problems. Recognizing this potential, a research was performed to develop an algorithm for solving a standard semi-infinite problem. The result was proposed in [16J] where an hybrid genetic/interval algorithm is described. The underlying idea is to conjugate a stochastic global optimization technique (a genetic algorithm) with a deterministic global one (an interval procedure) by means of a penalty method. The penalty functions are computed via an interval procedure and the resulting unconstrained problem is solved using a partially elitistic genetic algorithm [8J].
This hybrid approach has been successfully applied to the trajectory planning of mechanical manipulators [4C], [23P] and for the steering of car-like vehicles [6C]. An advantage of this technique with respect to conventional alternatives is that the estimated global solution of the semi-infinite problem is feasible with certainty. This feature is particularly significant in dealing with control systems design problems for uncertain systems [15J], [19P], [25J].

Trajectory planning for robot manipulators

Optimal trajectory planning of rigid-links manipulators can be applied to industrial robots to achieve high-performance in manufacturing production. With this aim, using interpolation schemes with cubic splines in the joint space, minimum-time problems with velocity, acceleration, and jerk constraints has been addressed in [10J], [15P], [16P] or when travelling time is fixed, minimum-jerk problems have been posed [12J], [17P]. The proposed algorithms use interval routines that find global solutions [12J].
The minimum-time problem has also been addressed considering dynamic constraints [23J], [4C], [23P]. In this case, the algorithmic solutions are based on the mixed genetic/interval approach presented in [16J].

Inversion-based control

Inversion-based control is a feedforward methodology. Its development started in the 90’s as a new technique to achieve high-performance in the control or regulation of systems. The basic idea is to define the desired output signal according to the addressed application with the aim to determine the input that, when applied to the nominal system, causes the desired output. The procedure to determine this inverse input is usually called input-output (stable) dynamic inversion.
Inversion-based control has been applied to the regulation of minimum-phase linear scalar systems in [18J]. To this aim, “transition” polynomials are introduced as desired outputs. They are a family of polynomials parametrized by a transition time that allows a smooth transition between two constant output values. Extension to the nonminimum-phase case was addressed in [29J] where the input-output inversion is stable, i.e. the sought inverse input is bounded despite the presence of an unstable zero dynamics. In both works [18J] and [29J], the inverse input has been determined by closed-form expressions depending on the the transiotion time. This has allowed a straightforward solution to a time-optimal regulation problem with constraints on the input and its derivatives. The inverse input exhibits the so-called pre-action and post-action when the regulated system has unstable and stable zero dynamics respectively. A MATLAB-based implementation of transition polynomials and stable input-output inversion has been presented in [33J], [46P], [50P]. A more general closed-form expression of the inverse input is presented in [47P] where the desired output can be an arbitrary bounded function satisfying a relative degree condition.
A smooth feedforward regulation problem for linear multivariable plants is addressed in [48P]. Using transition polynomials as desired outputs, Pareto optimal inverse inputs are found by minimizing the output transition times with amplitude constraints on the inputs and their derivatives.
A possible cause of output errors in the implementation of inversion-based control is the unreliability of the nominal system (over which the inversion procedure is applied) due to system’s parameter variations. To overcome this difficulty, a recursive estimation of the system’s parameters is presented in [56P]. It uses a gradient descend algorithm to minimize the integrated square error of the regulated output.
The inverse input can be applied as the command input of a closed-loop PID control system in order to improve the regulation performances. In [61P], the three parts of the inverse input, i.e., the preaction, the central action, and the postaction are proposed to be implemented as causal step-responses of second-order filters. This significantly facilitates the implementation in an industrial context such as, e.g., when Distributed Control System (DCS) is on the use.
A dynamic path inversion problem, a variant ot the standard input-output inversion problem, has been proposed for a class of nonlinear systems in [39P]. Given a path on the system output space, the problem is to find a feedforward input that causes the system to follow the given path. Solution conditions and constructive procedures are given by paper [39P] to solve this inversion problem both locally and globally.

Path generation and motion control for autonomous wheeled vehicles

Considering a kinematics model of a car-like vehicle, a dynamic path inversion procedure is presented in [32P]. It is shown how to determine the vehicle’s steering input in such a way a front point of the vehicle exactly follows a pre-specified Cartesian path. Motivation for this special motion planning problem arises from the need of vision-based autonomous vehicle driving. In [37P], [27J], an extension of this technique has led to a path following control scheme where the feedforward action is given by the dynamic path inversion of [32P] and the feedback is proportional to the trajectory tangential and normal errors. A quantitative convergence analysis has been also carried out by considering an uncertain model of the car-like vehicle.
The vision-based automatic driving of the ARGO car, a vehicle prototype of the University of Parma, has been described in [11J]. In particular, its motion control system which is based on a look-ahed gain-scheduled controller is presented. The more advanced iterative trajectory control that uses the path generation with quintic G2-splines [21J] is also outlined. A method for sensing obstacles and vehicles based on artificial vision has been presented with the implementation for the platooning of the ARGO vehicle [14J]. The adopted gain-scheduled controller for automatic steering is also decribed.
Trajectory tracking of wheeled mobile robots is the topic of [63P]. Feedforward inverse control and recursive convex replanning of the reference trajectory are proposed to form a hybrid control scheme when only discrete-time low-frequency measurements of the robot state are available. This scheme applied to the standard unicycle model is shown to maintain its efficacy also in presence of noise or unmodeled robot dynamics. Explicit, sufficient conditions are also provided to ensure global boundedness of the tracking error. Experimental resuts are included to highlight the new approach.
A new motion planning primitive, the quintic G2-spline, has been introduced in [27P], [21J] for the iterative steering of vision-based autonomous vehicles. It is a parameterized fifth-order polynomial curve that makes possible to exactly interpolate any sequence of Cartesian points with associated arbitrary unit tangent vector and curvature. In such a way, the resulting composite path has an overall second-order geometric continuity (G2-continuity). A supervisory strategy for iterative steering that integrates vision data processing feedback with inversion-based feedforward is described in [21J]. The quintic G2-spline depends on four (eta) parameters that can be selected to achieve an optimal path planning. An example of this optimization is described in [28P] where the maximum of the curvature derivative with respect to the arc length is minimized. The dynamic path inversion control of a wheeled omnidirectional robot is presented in [44P] with the use of quintic G2-splines to achieve a robot’s smooth motion.
Using smooth continuous-acceleration inputs for a unicycle mobile robots (UMRs), it is shown in [28J], [35P] that a robot’s path is feasible, apart kinematics singularities, if and only if it is a path with third-order geometric continuity (G3-path). This continuity accounts for a path whose unit tangent vector, curvature, and curvature derivative with respect to the arc length are continuous along the path. This path property justifies the use of eta3-splines in the dynamic path inversion procedure for the UMR. Eta3-splines were originaly presented in [41P] and subsequently in [32J], [8C] with more details. The eta3-spline, which is a generalization of the eta2-spline (or quintic G2 spline), is a seventh-order polynomial curve that interpolates arbitrary Cartesian points with associated arbitrary unit tangent vectors, curvatures, and curvature derivatives. It depends on six (eta) parameters that can be freely chosen to shape the spline without changing the interpolation conditions at the curve endpoints. Papers [32J], [8C] also give a glimpse on the more general eta-k-splines.
In [67P] an application of eta3-splines to the autonomous parking of car-like vehicles is presented. It proposes a multi-optimization approach to build up intrinsically feasible path maneuvers over which to minimize the total length of parking paths and the maximum absolute values of curvature and curvature derivative. This approach takes into account the mandatory constraint of obstacle avoidance and maximal steering angle and the constraint of maximal curvature derivative which is a selectable limit to ensure the desired smoothness of the parking paths.
Generation of high-quality drive paths for a truck and trailer vehicle is the topic of [38J]. First, it is shown the need of generating G4-paths, i.e. paths with fourth-order geometric continuity. Secondly, eta4-splines are developed and presented to be used in the generation of the trailer G4-paths. The method gives the explicit closed-form expressions of the smooth feedforward control to drive the articulated vehicle between arbitrary dynamic configurations along a path that can be shaped with the free parameters of the eta4-spline.

Time-optimal constrained feedforward control

A line of research has considered the time-optimal (i.e. minimum-time) feedforward control of linear plants with input and output constraints. Preliminarily results were presented in [54P], [34J] where the minimum-time rest-to-rest control of a flexible joint is addressed and comparisons with the inversion-based control solution are discussed.
For linear scalar systems, a more general approach was presented in [52P], [53P] and with full details in [35J]. In addressing the minimum-time feedforward control problem with input constraints only, the well-known solution is the so-called bang-bang control. When output constraints are also considered as in [35J], the emerging solution is the generalized bang-bang control, i.e. the time-optimal input is characterized by a sequence of bang-bang functions and linear combinations of the system zero modes. By time–discretization, an approximate determination of the generalized bang-bang control can be found by solving a sequence of linear programming feasibility problems. Generalized bang-bang control has the potential to improve the regulation performances of a large class of engineering control problems, also comprising mechatronics and process control problems.
The paper [69P] considers a minimum-time feedforward motion control problem for an open container carrying a liquid. The proposed solution, a form of generalized bang-bang control in a multivariable context, is a time-continuous acceleration planning that avoids liquid spilling and satisfies amplitude constraints on jerk, acceleration, and velocity of the container moving on a linear guide of an automation line. The devised solution can provide rest-to-rest liquid motion planning or, alternatively, a rest-to-disequilibrium planning with bounded post-motion liquid oscillations. Experimental results on a test bench have shown the practicability and the effectiveness of the approach.
The time-optimal trajectory planning of an automatic guided vehicle (AGV) on a given feasible path while respecting velocity, acceleration and jerk constraints is addressed in [65P]. A theoretical result showing the connection between the geometric continuity of AGV paths and the smoothness of its control inputs (linear velocity and steering angle of the AGV motor wheel) is established. The solution hence proposed for the optimal planning is based on a dynamic path inversion algorithm for which first the optimal velocity profile is determined and then the optimal steering signal is derived from a geometrical construction. Eta3-splines [32J] can be effectively used to plan the desired paths in this AGV application.

Feedforward-feedback control

A possible classification of contributions on this topic is as follows:
1. Inverse feedforward and feedback control for uncertain systems
2. Inverse feedforward and PID feedback control
3. Inverse feedforward and feedback control for multivariable systems
4. Iterative output replanning
5. Generalized bang-bang control for closed-loop systems

1. Inverse feedforward and feedback control for uncertain systems
This line of research has been mainly devoted to devise new high-performance feedforward-feedback schemes for the fundamental problem of set-point regulation. The main idea is to use feedforward and feedback in a combined way. Feedback has to reduce the sensitivity of the (system) plant to disturbances, unmodelled dynamics, and parameter variations whereas feedforward improves the transient responses on the plant output. Specifically, the feedforward action, which is applied to the command input of the (feedback) closed-loop, is proposed to be determined by inversion-based control (i.e., by input-output stable dynamic inversion) or alternatively by time-optimal constrained control.
The majority of contributions addresses the former case. A first scheme addressing minimum-phase plants appeared in 1998 [20P] and with full details in [17J]. The case of scalar regulation of linear, stable, nonminimum-phase plants that are affecteted by uncertain parameters is preliminarily addressed in [26P], [29P]. A complete exposition of the design method, which comprises transition polynomials [18J] as desired outputs, is presented in 2001 in [19J]. The feedback controller and the feedforward inverse input are designed together to minimize the worst-case settling-time of the set-point transition. Constraints on the amplitude of the control variable and on maximum overshoot and undershoot are taken into account. Extensions to possibly unstable plants are presented in [33P] and [31J]. In these works, the feedback parts of the overall design use the linear quadratic regulator theory and the H-infinity control theory respectively. It is worth emphasizing that all the proposed inversion-based feedforward-feedback schemes adhere to the internal model principle so that robust steady-state regulation is ensured. This feature appears an improvement over the first inversion-based feedforward-feedback scheme proposed in 1996 by Devasia and co-researchers (cf. doi 10.1109/9.508898).

2. Inverse feedforward and PID feedback control
An application to a DC motor-position servo is presented in [22J]. Two feedforward-feedback schemes are designed and experimented. One scheme adopts a standard PD feedback controller and the inverse feedforward improves the servo performances. The second scheme that couples an high-gain feedback controller with the inverse feedforward significantly improves over the first one also exhibiting better robustness against the possible variation of the motor inertia. In [26J], a PID feedback controller is considered to be already designed. Then, the numerical identification of the closed-loop system permits to determine the command input from the desired output signal. The works in [38P], [45P], [49P], [30J] propose to enhance PID control for set-point regulation by setting coordinated designs. The PID feedback controller is tuned to achieve the best load rejection while inversion-based feedforward establishes high-performances in the regulation transients. The methodology gives as a tuning parameter the transition time that can effectively handle the trade-off between performance and control activity. Detailed closed-form expressions of the inverse command input, which are expecially useful in industrial implementations, are reported in [30J].
A cascade control system with two nested PID feedback loops is considered in [51P]. The command signal applied to the outer loop is determined by stable inversion having previously approximated the process dead time dynamics with Padé approximants. The design of an industrial control system is addressed with a new methodology in [58P], [59P]. This integrates identification, PID feedback tuning based on frequency loop shaping and (noncausal) inversion-based feedforward.

3. Inverse feedforward and feedback control for multivariable systems
Multivariable regulation is addressed for two-inputs two-outputs (TITO) plants in [31P] and in [36P] more generally for multi-inputs multi-outputs (MIMO) nonminimum-phase plants affected by uncertain parameters. The proposed control architecture consists of a high-gain decoupling feedback controller and a vector command input determined by stable inversion. This architecture, which enforces the internal model principle, is finally tuned to minimize the worst-case settling-time subject to amplitude limits on the control variables and constraints on the overshoots and undershoots of the outputs.

4. Iterative output replanning
Output tracking of nonlinear flat systems is the topic of [64P] and [36J]. When the system’s state can be acquired with low-frequency measurements, a hybrid feedforward-feedback control scheme is proposed. It is based on an inverse feedforward command input that is periodically updated by means of an iterative output replanning that uses interpolating Hermite polynomials. Theoretical convergence results, simulations and comparisons for the unicycle and the one-trailer system are presented.

5. Generalized bang-bang control for closed-loop systems
In [55P], PID feedback control is improved by applying to the closed-loop system a minimum-time feedforward input for which amplitude constraints on the plant’s input and output are satisfied. The proposed scheme considers a rest-to-rest transition for the overall closed-loop system. The time-optimal input is computed by solving a sequence of linear programming feasibility tests. An extension of this technique to MIMO plants is presented in [68P]. Two feedfoward/feedback schemes are devised. Both use decentralized PID feedback controllers. The first scheme is the direct extension of that in [55P]. For the second one, first the plant input is determined as the generalized bang-bang control relative to the plant (cf. [35J]), then the actual feedforward command input is determined by means of a dynamic inversion procedure.

Mechatronics applications

Inversion-based control has been successfully applied to various mechatronics devices. A series of works has focused on positioning control with elastic linkages. The aim is to achieve a fast rest-to-rest motion with suppression of final parasitic vibrations. Paper [13J] shows how to achieve it. Transition polynomials are used to plan the desired vibration-free motion. Then, the input motion that is implemented by a servo-actuator is deduced by input-output dynamic inversion. Minimum-time motion is also achieved by solving a bisection-type procedure with pertinent actuator constraints. Preliminarily results and variations of this approach have been presented in [18P], [21P], [24P]. In [20J] with the use of experiments for parameter estimation, an improvement that minimizes the maximal residual vibrations is proposed. An iterative approach for the determination of the input-output inversion feedforward control law for residual vibration reduction is proposed in [57P]. It is based on a gradient-based minimization of the integrated square error on the system output. Simulation results show the effectiveness of this approach.
Control of the transient sway and residual oscillations of a payload carried by an overhead crane is the topic of [24J], [25P]. These papers propose a feedforward-feedback scheme that comprises a robust observer-based feedback controller and an inversion-based feedforward command control. The control design based on this scheme uses transition polynomials and takes into account parameter uncertainties. Simulation results, based on a nonlinear crane model, show that the proposed method is also effective when the payload is hoisted or lowered during the motion, and when friction effects are considered.
The end-point control of a flexible link is addressed with inversion-based control in [30P]. The nonminimum-phase dynamics of the link end-point (output) commanded by the hub angle (input) has required applying a stable dynamic inversion procedure. The desired vibration-free motion modelled by transition polynomials has been optimized to obtain a minimum-time movement while given constraints on velocity and accelerations are satisfied. Experimental results, reported in [34P], show that the proposed method is effective and inherently robust to modelling errors.
Set-point regulation of a magnetic levitation apparatus is addressed with a combined feedforward-feedback approach in [62P]. An integral action and a state feedback set the structure of the closed-loop system. Its command input is determined by dynamic inversion with the use of transition polynomials. The design method exploits the nonlinear flatness of the levitation system and achieves a minimum-time in performing the required set-point regulation. Simulations highlight the robustness and good performances of the proposed approach.
The flux observer for induction servo motors is designed by a genetic algorithm in [8P]. This new approach makes the flux observer robust against variations of operational conditions and very large perturbations of the machine model parameters. Harmonic disturbance attenuation in the motion control of wood countouring machines is addressed in [10P]. Frequency- and time-domain specifications are dealt within a unified framework with the use of integral quadratic indexes. The controller design is then reduced to a mini-max optimization problem.

Vision-based automation

Paper [40P] shows how artificial vision can be successfully used for the automatic control of an industrial tunnel baking oven. The baking status of food products (biscuits in the experimental application) is estimated by data from a color line-scan camera placed at the outlet of the tunnel oven. Then, a feedback fuzzy strategy is designed to control the oven burners with the aim to achieve optimal baking. This research has been carried out in collaboration with Colussi s.p.a. (Perugia, Italy) in the 5th Framework Programme, EUropean Take-up of essential Information Society Technologies - Integrated Machine Vision (EUTIST-IMV).
A computer vision system for the inspection of locomotive pantographs, named PAVISYS, is described in [66P]. This automatic inspection algorithm consists of three main steps: a pantograph classifier, a modular quality assessment system, and a report generator. The paper provides details about this architecture, reporting the most significant experimental results obtained in the extensive set of lab tests that were run to assess its performances. The research that has led to PAVISYS was originally developed in collaboration with the Italian Train Network (RFI, Rete Ferroviaria Italiana). Presently, PAVISYS is the heart of the PANTOBOT system (Henesis srl, Parma, Italy), a full-fledged monitoring system for train pantographs, which also adds remote analysis and management of images coming from the Inspection Points located along the railway.

Velocity planning

Velocity planning for autonomous vehicles is the topic of [43P]. A minimum-jerk continuous-acceleration planning for a given travel distance is sought with arbitrary interpolation conditions on velocity and acceleration at the endpoints of an assigned time-interval. Interpolating cubic splines are used to obtain an approximate solution scheme which is suitable for real-time implementation by using a new heuristic optimization algorithm.
The paper in [37J] addresses the problem of minimum-time velocity planning subject to a jerk amplitude constraint and to arbitrary velocity and acceleration interpolation conditions. This problem which is relevant in the field of autonomous robotic navigation and also for linear one-dimensional mechatronics systems is dealt with an algebraic approach based on Pontryagin’s Maximum Principle. Specifically, it is shown that solution to this velocity planning can be found by solving a quartic algebraic equation. Hence, to compute the velocity profile a finite-step algorithm that is suitable for real-time applications is then presented. In [60P], this minimum-time velocity planning problem is considered by adding arbitrarily given constraints on velocity and acceleration. These constraints make the problem significantly more difficult. First, it is presented a sufficient condition for the feasibility of the constrained velocity planning. Then, an approximate solution is determined by time-discretization and by solving a sequence of linear programming feasibility tests.

Publications

Papers on International Journals

[1J] A. Piazzi, “A new solution to the regulator problem with output stability”, IEEE Transactions on Automatic Control, vol. AC-31, no. 4, pp. 341-342, April 1986.
[2J] G. Marro, A. Piazzi, “Comments on “Conditioned invariant subspaces, disturbance decoupling and solutions of rational matrix equations””, International Journal of Control, vol. 44, no. 6, pp. 1777-1778, December 1986.
[3J] G. Basile, G. Marro, A. Piazzi, “Revisiting the regulator problem in the geometric approach, part I - Disturbance localization by dynamic compensation and part II - Asymptotic tracking and regulation in the presence of disturbances ”, Journal of Optimization Theory and Applications, vol. 53, no. 1, pp. 9-36, April 1987.
[4J] G. Basile, G. Marro, A. Piazzi, “Stability without eigenspaces in in the geometric approach: the regulator problem”, Journal of Optimization Theory and Applications, vol. 64, no. 1, pp. 29-42, January 1990.
[5J] A. Piazzi, “Pole placement under structural constraints”, IEEE Transactions on Automatic Control, vol. AC-35, no. 6, pp. 759-761, June 1990.
[6J] A. Piazzi, “Geometric aspects of reduced-order compensators for disturbance rejection”, IEEE Transactions on Automatic Control, vol. 36, no. 1, pp. 102-106, January 1991.
[7J] A. Piazzi, “Decision algebra and robust stability analysis”, Control and Computers, vol. 20, no. 3, pp. 89-96, 1992.
[8J] R. Menozzi, A. Piazzi, F. Contini, “Small-signal modeling for microwave FET linear circuits based on a genetic algorithm”, IEEE Transactions on Circuits and Systems Part I: Fundamental Theory and Applications, vol. 43, no. 10, pp. 839-847, October 1996.
[9J] A. Piazzi, G. Marro, “Robust stability using interval analysis”, International Journal of Systems Science, vol. 27, no. 12, pp. 1379-1388, December 1996.
[10J] A. Piazzi, A. Visioli, “Global minimum-time trajectory planning of mechanical manipulators using interval analysis”, International Journal of Control, vol. 71, no. 4, pp. 631-652, 1998.
[11J] A. Broggi, M. Bertozzi, A. Fascioli, C. Guarino Lo Bianco, A. Piazzi, “The ARGO autonomous vehicle’s vision and control systems”, International Journal of Intelligent Control and Systems, vol. 3, no. 4, pp. 409-441, 1999.
[12J] A. Piazzi, A. Visioli, “Global minimum-jerk trajectory planning of robot manipulators”, IEEE Transactions on Industrial Electronics, vol. 47, no. 1, pp. 140-149, February 2000.
[13J] A. Piazzi, A. Visioli, “Minimum-time system inversion based motion planning for residual vibration reduction”, IEEE/ASME Transactions on Mechatronics, vol. 5, no. 1, pp. 12-22, March 2000.
[14J] A. Broggi, M. Bertozzi, A. Fascioli, C. Guarino Lo Bianco, A. Piazzi, “Visual perception of obstacles and vehicles for platooning”, IEEE Transactions on Intelligent Transportation Systems, vol. 1, no. 3, pp. 164-176, September 2000.
[15J] C. Guarino Lo Bianco and A. Piazzi, “A global optimization approach to scalar H2/H-infinity control”, European Journal of Control, vol. 6, no. 4, pp. 356-367, 2000.
[16J] C. Guarino Lo Bianco, A. Piazzi, “A hybrid algorithm for infinitely constrained optimization”, International Journal of Systems Science vol. 32, no. 1, pp. 91-102, January 2001.
[17J] A. Piazzi, A. Visioli, “Robust set-point constrained regulation via dynamic inversion”, International Journal of Robust and Nonlinear Control, vol. 11, no. 1, pp. 1-22, January 2001.
[18J] A. Piazzi, A. Visioli, “Optimal noncausal set-point regulation of scalar systems”, Automatica, vol. 37, no. 1, pp. 121-127, January 2001.
[19J] A. Piazzi, A. Visioli, “Optimal inversion-based control for the set-point regulation of nonminimum-phase uncertain scalar systems”, IEEE Transactions on Automatic Control, vol. 46, no. 10, pp. 1654-1659, October 2001.
[20J] A. Piazzi, A. Visioli, “Point-to-point motion planning for servosystems with elastic transmission via optimal dynamic inversion”, ASME Journal of Dynamics Systems, Measurements, & Control, vol. 123, pp. 733-736, December 2001.
[21J] A. Piazzi, C. Guarino Lo Bianco, M. Bertozzi, A. Fascioli, A. Broggi, “Quintic G2-splines for the iterative steering of vision-based autonomous vehicles”, IEEE Transactions on Intelligent Transportation Systems, vol. 3, no. 1, pp. 27-36, March 2002.
[22J] C. Guarino Lo Bianco, A. Piazzi, “A servo control system design using dynamic inversion”, Control Engineering Practice, vol. 10, no. 8, pp. 847-855, August 2002.
[23J] C. Guarino Lo Bianco, A. Piazzi, “Minimum-time trajectory planning of mechanical manipulators under dynamic constraints”, International Journal of Control, vol. 75, no. 13, pp. 967-980, September 2002.
[24J] A. Piazzi, A. Visioli, “Optimal dynamic-inversion-based control of an overhead crane”, IEE Proceedings on Control Theory and Applications, vol. 149, no. 5, pp. 405-411, September 2002.
[25J] C. Guarino Lo Bianco, A. Piazzi, “Worst-case optimal static output feedback for uncertain systems”, Optimization and Engineering, vol. 3, no. 4, pp. 379–393, December 2002.
[26J] A. Visioli, A. Piazzi, “Improving set-point-following performance of industrial controllers with a fast dynamic inversion algorithm”, Industrial and Engineering Chemistry Research, vol. 42, no. 7, pp. 1357-1362, 2003.
[27J] L. Consolini, A. Piazzi, M. Tosques, “Path following of car-like vehicles using dynamic inversion”, International Journal of Control, vol. 76, no. 17, pp. 1724–1738, November 2003.
[28J] C. Guarino Lo Bianco, A. Piazzi, M. Romano, “Smooth motion generation for unicycle mobile robots via dynamic path inversion”, IEEE Transactions on Robotics, vol. 20, no. 5, pp. 884-891, October 2004.
[29J] A. Piazzi, A. Visioli, “Using stable input-output inversion for minimum-time feedforward constrained regulation of scalar systems”, Automatica, vol. 41, no. 2, pp. 305-313, February 2005; DOI 10.1016/j.automatica.2004.10.009
[30J] A. Piazzi, A. Visioli, “A noncausal approach for PID control”, Journal of Process Control, vol. 16, no. 8, pp. 831-843, September 2006.
[31J] A. Piazzi, A. Visioli, “Combining H-infinity control and dynamic inversion for robust constrained set-point regulation”, International Journal of Tomography & Statistics, vol. 6, no. S07, pp. 63-68, 2007.
[32J] A. Piazzi, C. Guarino Lo Bianco, M. Romano, “Eta3-splines for the smooth path generation of wheeled mobile robots”, IEEE Transaction on Robotics, vol. 23, no. 5, pp. 1089-1095, October 2007; DOI 10.1109/TRO.2007.903816
[33J] A. Visioli, A. Piazzi, “A toolbox for input-output system inversion”, International Journal of Computers, Communications & Control, vol. 2, no. 4, pp. 375-389, December 2007.
[34J] L. Consolini, O. Gerelli, C. Guarino Lo Bianco, A. Piazzi, “Flexible joints control: A minimum-time feed-forward technique”, Mechatronics, vol. 19, pp. 348-356, 2009.
[35J] L. Consolini, A. Piazzi, “Generalized bang-bang control for feedforward constrained regulation”, Automatica, vol. 45, no. 10, pp. 2234-2243, October 2009; DOI 10.1016/j.automatica.2009.06.030
[36J] L. Consolini, G. Lini, A. Piazzi, “Hermite polynomials for iterative output replanning for flat systems affected by additive noise”, Asian Journal of Control, vol. 15, no. 1, pp. 292-301, January 2013.
[37J] G. Lini, A. Piazzi, L. Consolini, “Algebraic solution to minimum-time velocity planning”, International Journal of Control, Automation, and Systems, vol. 11, no. 4, pp.805-814, 2013; DOI 10.1007/s12555-011-0065-y
[38J] F. Ghilardelli, G. Lini, A. Piazzi, “Path generation using eta4-splines for a truck and trailer vehicle”, IEEE Transactions on Automation Science and Engineering, vol. 11, no. 1, pp. 187-203, January 2014; DOI 10.1109/TASE.2013.2266962

Papers and Chapters on Books

[1C] G. Basile, G. Marro, A. Piazzi, “Stability without eigenspaces in the geometric approach: some new results”, in C.I. Byrnes, A. Linquist (Eds.) Frequency Domain and State Space Methods for Linear Systems, Elsevier Science Publisher B.V. (North-Holland), pp. 441-450, 1986.
[2C] G. Marro, A. Piazzi, “Il problema del regolatore”, su Teoria dei Sistemi e del Controllo di G. Marro, editore Zanichelli, Capitolo 7, pp. 228-269, 1989.
[3C] G. Marro, A. Piazzi, “Feedback systems stabilizability in terms of invariant zeros”, in A. Isidori, T.J. Tarn (Eds.) Systems, Models and Feedback: Theory and Applications, Birkhauser, Boston (U.S.A.), pp. 323-338, 1992.
[4C] C. Guarino Lo Bianco, A. Piazzi, “A semi-infinite optimization approach to optimal spline trajectory planning of mechanical manipulators”, Chapter 13 in M.A. Goberna, M.A. Lopez (Eds.) Semi-infinite Programming: Recent Advances, Kluwer Academic Publishers, pp. 271--297, 2001.
[5C] A. Piazzi, A. Visioli, “An interval analysis based algorithm for computing the stability margin of uncertain systems”, in I. Dimov, I. Lirkov, S. Margenov, Z. Zlatev (Eds.) Numerical Methods and Applications, Springer-Verlag, Berlin, pp. 246-254, 2003.
[6C] C. Guarino Lo Bianco, A. Piazzi, “Using semi-infinite optimization for the steering of car-like vehicles”, in J. Guddat, H. Th. Jongen, J.-J. Rückmann, M. Todorov (Eds.) Parametric Optimization and Related Topics VII, Sociedad Matemática Mexicana, pp. 121-132, 2004.
[7C] G. Lini, A. Piazzi, “Minimum-time velocity planning with arbitrary boundary conditions”, Chapter 26 in K.R. Kozlowski (Ed.) Robot Motion and Control 2009, Springer, ISBN 978-1-84882-984-8, pp. 287-296, 2009.
[8C] A. Piazzi, C. Guarino Lo Bianco, M. Romano, “Smooth path generation for wheeled mobile robots using eta3-splines”, Chapter 4 in F. Casolo (Ed.) Motion Control, In-Teh, Vukovar (Croatia), ISBN 978-953-7619-55-8, pp. 75-96, 2010.

Papers on International Conference Proceedings

[1P] G. Basile, G. Marro, A. Piazzi, “A new solution to disturbance localization problem with stability and its dual”, Proceedings of the '84 International AMSE Conference on Modelling and Simulation, Athens (Greece), vol. 1.2, pp. 19-27, June 1984.
[2P] G. Marro, A. Piazzi, “Duality of reduced-order regulators”, Proceedings of the '88 International AMSE Conference on Modelling and Simulation, Istanbul (Turkey), vol. 1B, pp. 113-121, June 1988.
[3P] G. Marro, A. Piazzi, “A geometric approach to robust regulation”, Proceedings of IFAC Conference “System Structure and Control: State-Space and Polynomials Methods”, Praha (Czech Republic), pp. 45-48, September 1989.
[4P] A. Piazzi, G. Marro, “The role of invariant zeros in multivariable system stability”, Proceedings of the first European Control Conference, Grenoble (France), publisher Hermes, vol. 1, pp. 383-387, 2-5 July 1991.
[5P] F. Persiani, A. Piazzi, “An evolutionist approach to the synthesis of stabilizing neural controllers”, Proceedings of the '91 International AMSE Conference “Signals & System”, Warsaw (Poland), vol. 2, pp. 213-222, 15-17 July 1991.
[6P] A. Piazzi, “Decomposability of controlled invariants: an application to the regulator problem with disturbance decoupling”, Proceedings of the '91 International AMSE Conference “Signals & System”, Warsaw (Poland), vol. 2, pp. 133-143, 15-17 July 1991.
[7P] G. Marro, A. Piazzi, “Regulation without transients under large parameter jumps”, Proceedings of the 12th WORLD IFAC CONGRESS, Sidney (Australia), vol. 4, pp. 23-26, 19-23 July 1993.
[8P] G. Franceschini, A. Piazzi, C. Tassoni, “A genetic algorithm approach to design flux observers for induction servo motors”, Proceedings of the 20th IEEE International Conference on Industrial Electronics, Control and Instrumentation, Bologna (Italy), vol. 3, pp. 2132-2136, 5-9 September 1994.
[9P] A. Piazzi, G. Marro, “On computing the robust decay rate of uncertain systems”, Proceedings of the IFAC Symposim on Robust Control Design, Rio de Janerio (Brazil), pp. 46-51, 14-16 September 1994.
[10P] M. Dilda, A. Piazzi, “Using quadratic indexes in the synthesis of harmonic disturbance attenuation compensators”, Proceedings of the 8th IEEE Mediterranean Electrotechnical Conference, Bari (Italy), vol. 1, pp. 256-261, 13-16 May 1996.
[11P] G. Marro, A. Piazzi, “A geometric approach to multivariable perfect tracking”, Proceedings of the 13th IFAC World Congress, San Francisco (U.S.A.), vol. C, pp. 241-246, 30 June – 5 July 1996.
[12P] R. Menozzi, A. Piazzi, “On the use of a genetic algorithm for millimeter-wave FET modeling”, Proceedings of the 26th European Solid State Device Research Conference, Bologna (Italy), pp. 663-666, 9-11 September 1996.
[13P] C. Guarino Lo Bianco, A. Piazzi, “A hybrid genetic/interval algorithm for semi-infinite optimization”, Proceedings of the 35th IEEE Conference on Decision and Control, Kobe (Japan), pp. 2136-2138, 11-13 December 1996.
[14P] C. Guarino Lo Bianco, A. Piazzi, “Mixed H2/H-infinity fixed-structure control via semi-infinite optimization”, Proceedings of the 7th IFAC Symposium on Computer Aided Control Systems Design, Gent (Belgium), pp. 329-334, 28-30 April 1997.
[15P] A. Piazzi, A. Visioli, “A global optimization approach to trajectory planning for industrial robots”, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems, Grenoble (France), vol. 3, pp. 1553-1559, 7-11 September 1997.
[16P] A. Piazzi, A. Visioli, “A cutting-plane algorithm for minimum-time trajectory planning of industrial robots”, Proceedings of the 36th IEEE Conference on Decision and Control, San Diego CA (USA), vol. 2, pp. 1216-1218, 10-12 December 1997.
[17P] A. Piazzi, A. Visioli, “An interval algorithm for minimum-jerk trajectory planning of robot manipulators”, Proceeding of the 36th IEEE Conference on Decision and Control, San Diego CA (USA), vol. 2, pp. 1924-1927, 10-12 December 1997.
[18P] A. Piazzi, A. Visioli, “Minimum-time open-loop smooth control for point-to-point motion in vibratory systems”, Proceedings of the 1998 IEEE International Conference on Robotics and Automation, Leuven (Belgium), vol. 2, pp. 946-951, 16-20 May 1998.
[19P] C. Guarino Lo Bianco, A. Piazzi, “A worst-case approach to SISO mixed H2/H-infinity control”, Proceedings of the 1998 IEEE Conference on Control Applications, Trieste (Italy), vol. 1, pp. 684-688, 1-4 September 1998.
[20P] A. Piazzi, A. Visioli, “A system inversion approach to robust set-point regulation”, Proceeding of the 37th IEEE Conference on Decision and Control, Tampa (Florida), pp. 3849-3854, 16-18 December 1998.
[21P] A. Piazzi, A. Visioli, “Optimal system inversion based motion planning for servosystems with elastic transmission”, The 2nd International Conference on Recent Advances in Mechatronics, Istanbul (Turkey), pp. 28-33, 24-26 May 1999.
[22P] C. Guarino Lo Bianco, A. Piazzi, “A global optimization approach to scalar H2/H-infinity control”, Proceedings of the European Control Conference, Karlshure (Germany), 30 August - 3 September 1999.
[23P] C. Guarino Lo Bianco, A. Piazzi, “A genetic/interval approach to optimal trajectory planning of industrial robots under torque constraints”, Proceedings of the European Control Conference, Kurlshure (Germany), 30 August - 3 September 1999.
[24P] A. Piazzi, A. Visioli, “Worst-case optimal noncausal motion planning for residual vibration reduction”, Proceedings of the European Control Conference, Kurlshure (Germany), 30 August - 3 September 1999.
[25P] A. Piazzi, A. Visioli, “System inversion based control of an overhead crane”, Proceedings of the IFAC Conference on Control Systems Design, Bratislava (Slovakia), pp. 185-190, 18-20 June 2000.
[26P] A. Piazzi, A. Visioli, “On the set-point regulation of uncertain nonminimum-phase scalar systems”, Proceedings of the IFAC Symposium on Robust Control Design, Prague (Czech Republic), 21-23 June 2000.
[27P] A. Piazzi, C. Guarino Lo Bianco, “Quintic G2-splines for trajectory planning of autonomous vehicles”, Proceedings of the IEEE 2000 Intelligent Vehicles Symposium, Dearborn MI (USA), pp. 198-203, 4-5 October 2000.
[28P] C. Guarino Lo Bianco, A. Piazzi, “Optimal trajectory planning with quintic G2-splines”, Proceedings of the IEEE 2000 Intelligent Vehicles Symposium, Dearborn MI (USA), pp. 620-625, 4-5 October 2000.
[29P] A. Piazzi, A. Visioli, “Noncausal robust set-point regulation of nonminimum-phase scalar systems”, Proceedings of the 39th IEEE Conference on Decision and Control, Sydney (Australia), pp. 4098-4103, 13-16 December 2000.
[30P] A. Piazzi, A. Visioli, “End-point control of a flexible-link via optimal dynamic inversion”, Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Como (Italy), pp. 936-941, 8-11 July 2001.
[31P] A. Piazzi, A. Visioli, “A dynamic inversion approach to robust set-point regulation of TITO systems”, Proceedings of the European Control Conference, Porto (Portugal), pp. 2676-2681, 4-7 September 2001.
[32P] L. Consolini, A. Piazzi, M. Tosques, “Motion planning for steering a car-like vehicle”, Proceedings of the European Control Conference, Porto (Portugal), pp. 1834-1839, 4-7 September 2001.
[33P] A. Piazzi, A. Visioli, “LQ-based set-point constrained regulation of uncertain systems via dynamic inversion”, Proceedings of the European Control Conference, Porto (Portugal), pp. 3481-3485, 4-7 September 2001.
[34P] A. Piazzi, A. Visioli, “Flexible link end-point control based on exact dynamic inversion: experimental results”, Proceedings of IMECE’01 International Mechanical Engineering Congress and Exposition, New York, USA, 11-16 November 2001.
[35P] C. Guarino Lo Bianco, A. Piazzi, “Inversion-based control of wheeled mobile robots”, Proceedings of the IEEE 2002 Intelligent Vehicles Symposium, Versailles (France), pp. 190-195, 18-20 June 2002.
[36P] A. Piazzi, A. Visioli, "Robust multivariable set-point regulation via stable dynamic inversion", Proceedings of the 15th IFAC World Congress on Automatic Control, Barcelona (Spain), 21-26 July 2002.
[37P] L. Consolini, A. Piazzi, M. Tosques, "A dynamic inversion based controller for path-following of car-like vehicles", Proceedings of the 15th IFAC World Congress on Automatic Control, Barcelona (Spain), 21-26 July 2002.
[38P] A. Piazzi, A. Visioli, “Improved PI control via dynamic inversion”, Proceedings of the 15th IFAC World Congress on Automatic Control, Barcelona (Spain), 21-26 July 2002.
[39P] L. Consolini, M. Tosques, A. Piazzi, “Dynamic path inversion for a class of nonlinear systems”, Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas (Nevada, USA), pp. 3831-3836, 10-13 December 2002.
[40P] C. Guarino Lo Bianco, M. Romano, A. Piazzi, “Vision-based feedback control strategy for an industrial band oven”, Proceedings of the European Control Conference, Cambridge (UK), 1-4 September 2003.
[41P] A. Piazzi, M. Romano, C. Guarino Lo Bianco, “G3-splines for the path planning of wheeled mobile robots”, Proceedings of the European Control Conference, Cambridge (UK), 1-4 September 2003.
[42P] A. Piazzi, A. Visioli, “A toolbox for computing the stability margin of uncertain systems”, Proceedings of the European Control Conference, Cambridge (UK), 1-4 September 2003.
[43P] C. Guarino Lo Bianco, A. Piazzi, M. Romano, “Velocity planning for autonomous vehicles”, Proceedings of the IEEE 2004 Intelligent Vehicles Symposium, Parma (Italy), pp. 413-418, 14-17 June 2004.
[44P] C. Guarino Lo Bianco, A. Piazzi, M. Romano, “Smooth control of a wheeled omnidirectional robot”, Proceedings of the IFAC 2004 Intelligent Autonomous Vehicles Conference, Lisboa (Portogal), 5-7 July 2004.
[45P] A. Piazzi, A. Visioli, “A noncausal approach to the improvement of PID control performances” Proceedings of the 2004 American Control Conference, Boston (USA), pp. 4022-4027, 30 June – 2 July 2004.
[46P] A. Piazzi, A. Visioli, L. Ciobani, “A toolbox for dynamic inversion based control system design”, Proceedings of the 39th IEEE Conference on Decision and Control, Paradise Island (Bahamas), pp. 1289—1294, 13-17 December 2004.
[47P] D. Pallastrelli, A. Piazzi, “Stable dynamic inversion of nonminimum-phase scalar linear systems”, Proceedings of the 16th IFAC World Congress, Prague (Czech Republic), 4-8 July 2005.
[48P] A. Piazzi, A. Visioli, “Pareto optimal feedforward constrained regulation of MIMO linear systems”, Proceedings of the 16th IFAC World Congress, Prague (Czech Republic), 4-8 July 2005.
[49P] A. Visioli, A. Piazzi, “On the use of dynamic inversion for the improvement of PID control”, Proceedings of the 16th IFAC World Congress, Prague (Czech Republic), 4-8 July 2005.
[50P] A. Piazzi, A. Visioli, “A toolbox for input-output system inversion”, Proceedings of the 7th IFAC Symposium on Advances in Control Education, Madrid (Spain), 21-23 June 2006.
[51P] A. Visioli, A. Piazzi, “An automatic tuning method for cascade control systems”, Proceedings of the 2006 IEEE International Conference on Control Applications, Munich (Germany), pp. 2968-2973, 4-6 October 2006.
[52P] L. Consolini, A. Piazzi, “Minimum-time feedforward control with input and output constraints”, Proceedings of the 2006 IEEE Conference on Computer Aided Control Systems Design, Munich (Germany), pp. 1538-1543, 4-6 October 2006.
[53P] L. Consolini, A. Piazzi, “Generalized bang-bang control for feedforward constrained regulation”, Proceedings of the 45th IEEE Conference on Decision and Control, San Diego (California USA), pp. 893-898, 13-15 December 2006.
[54P] L. Consolini, O. Gerelli, C. Guarino Lo Bianco, A. Piazzi, “Minimum-time control of flexible joints with input and output constraints”, Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Roma (Italy), pp. 3811-3816, 10-14 April 2007.
[55P] L. Consolini, A. Piazzi, A. Visioli, “Minimum-time feedforward control for industrial processes”, Proceedings of the European Control Conference, Kos (Greece), pp. 5282-5287, 2-5 July 2007.
[56P] A. Piazzi, A. Visioli, “An iterative approach for noncausal feedforward tuning”, Proceedings of the American Control Conference, New York (USA), pp. 1251-1256, 11-13 July 2007.
[57P] A. Visioli, A. Piazzi, “Iterative feedforward tuning for residual vibration reduction”, Proceedings of the 17th IFAC World Congress, Seoul (Korea), pp. 11829-11834, 6-11 July 2008.
[58P] C. Carnevale, A. Piazzi, A. Visioli, “A methodology for integrated system identification, PID controller tuning and noncausal feedforward control design”, Proceedings of the 17th IFAC World Congress, Seoul (Korea), pp. 13324-13329, 6-11 July 2008.
[59P] C. Carnevale, A. Piazzi, A. Visioli, “Noncausal open-loop control with combined system identification and PID controller tuning”, Proceedings of the UKACC Control Conference, Manchester (UK), 2-4 September 2008.
[60P] G. Lini, L. Consolini, A. Piazzi, “Minimum-time constrained velocity planning”, Proceedings of the 17th Mediterranean Conference on Control & Automation, Thessaloniki (Greece), pp. 748-753, 24-26 June 2009.
[61P] M. Beschi, A. Piazzi, A. Visioli, "On the practical implementation of a noncausal feedforward technique for PID control", Proceedings of the European Control Conference, Budapest (Hungary), pp. 1806-1811, 23-26 August 2009.
[62P] A. Di Fluri, A. Piazzi, A. Visioli, "Feedforward/feedback control of a magnetic levitation apparatus", Proceedings of the European Control Conference, Budapest (Hungary), pp. 4593-4598, 23-26 August 2009.
[63P] M. Argenti, L. Consolini, G. Lini, A. Piazzi, “Recursive convex replanning for the trajectory tracking of wheeled mobile robots”, Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage (Alaska, USA), pp. 4916-4921, 3-8 May 2010.
[64P] L. Consolini, G. Lini, A. Piazzi, "Iterative output replanning for flat systems affected by additive noise", Proceedings of the 49th IEEE Conference on Decision and Control, Atlanta, (Georgia, USA), pp. 6248-6253, 15-17 December 2010.
[65P] G. Lini, A. Piazzi, "Time-optimal dynamic path inversion for an automatic guided vehicle", Proceedings of the 49th IEEE Conference on Decision and Control, Atlanta, (Georgia, USA), pp. 5264-5269, 15-17 December 2010.
[66P] M. Sacchi, S. Cagnoni, D. Spagnoletti, L. Ascari, G. Zunino, A. Piazzi, “PAVISYS: A computer vision system for the inspection of locomotive pantographs”, Proceedings of PACIFIC Conference, Pantograph Catenary Interaction Framework for Intelligent Control Conference, Amiens (France), 8 December 2011.
[67P] G. Lini, A. Piazzi, L. Consolini, “Multi-optimization of eta3-splines for autonomous parking”, Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, (Florida, USA), pp. 6367-6372, 12-15 December 2011; DOI 10.1109/CDC.2011.6161095
[68P] L. Consolini, G. Lini, A. Piazzi, A. Visioli, “Minimum-time rest-to-rest feedforward action for PID feedback MIMO systems”, Proceedings of the IFAC Conference on Advances in PID Control, Brescia (Italy), 28-30 March 2012.
[69P] L. Consolini, A. Costalunga, A. Piazzi, M. Vezzosi, “Minimum-time feedforward control of an open liquid container”, Proceedings of the 39th Conference of IEEE Industrial Electronics Society, Vienna (Austria), pp. 3590-3595, 10-13 November 2013; DOI 10.1109/IECON.2013.6699706

Papers on National Journals and Conference Proceedings

[1N] G. Basile, G. Marro, A. Piazzi, “Geometric approach to linear system analysis and sysnthesis'', su Identification, Control, Optimization of Dynamical Systems, 1988 Progress Report, pp. 18-20, ed. Pitagora (Bologna), Atti dell'Incontro Nazionale dei Ricercatori del Progetto Nazionale M.P.I., Villa Olmo, Como, settembre 1988.
[2N] A. Piazzi, F. Persiani, “Modellazione di curve e superfici con le spline parametriche beta2”, Il Progettista Industriale, Edizioni Tecniche Nuove (Milano), anno 12, n. 1, pp. 42-53, gennaio 1992.
[3N] C. Guarino Lo Bianco, M. Romano, A. Piazzi, E. Pinazzi, “Controllo di cottura alimentare in un forno industriale”, Automazione e Strumentazione, pp. 109-115, aprile 2004.

Technical Reports

[1R] A. Piazzi, “Geometric aspects of reduced-order compensators”, D.E.I.S., Università di Bologna, Report n. GA01/88, gennaio 1988.
[2R] A. Piazzi, F. Persiani, “Modellazione di curve e superfici con le spline parametriche beta2”, Laboratorio di CAD Avanzato, Facoltà di Ingegneria di Parma, Report n. CG01/89, novembre 1989.
[3R] G. Marro, A. Piazzi, “An invariant zeros interpretation of the internal stability in the disturbance localization problem and its dual”, D.E.I.S., Università di Bologna, Report n. TSC01/91, febbraio 1991.
[4R] M. Dilda, A. Piazzi, “Using quadratic indexes in the synthesis of harmonic disturbante attenuation controllers”, Dipartimento di Ingegneria dell'Informazione, Università di Parma, Report n. TSC01/95, ottobre 1995.
[5R] A. Piazzi, “A positivity-based algorithm for computing the stability margin of uncertain systems”, Dipartimento di Ingegneria dell'Informazione, Università di Parma, Report n. TSC01/96, luglio 1996.
[6R] A. Piazzi, A. Visioli, “Set-point regulation of scalar systems via optimal dynamic inversion”, Dipartimento di Ingegneria dell'Informazione, Università di Parma, Report n. TSC01-00, marzo 2000.
[7R] S. Gianferrari, C. Guarino Lo Bianco, A. Piazzi, “Modellistica e regolazione per bruciatori ad elevate prestazioni”, Dipartimento di Ingegneria dell'Informazione, Università di Parma, Report n. TSC02-00, luglio 2000.
[8R] M. Romano, C. Guarino Lo Bianco, A. Piazzi, “Vision-based feedback control system design of an industrial band oven”, Dipartimento di Ingegneria dell'Informazione, Università di Parma, Technical Report, Deliverable D.20.14 of the European Project COOKIES, April 2003.
[9R] M. Romano, C. Guarino Lo Bianco, A. Piazzi, R. Santi, “Quality control of baking status of oven products”, Dipartimento di Ingegneria dell'Informazione, Università di Parma, Public Final Report of EUTIST-IMV Activity COOKIES, European Project COOKIES, October 2003.
[10R] S. Cagnoni, A. Piazzi, M. Sacchi, “Ispezione automatica di pantografi per trazione elettrica mediante visione artificiale”, Dipartimento di Ingegneria dell'Informazione, Università di Parma, Relazione riassuntiva finale del progetto PAVISYS (convenzione di ricerca in collaborazione con Rete Ferroviaria Italiana RFI - Ferrovie dello Stato), ottobre 2008.
[11R] F. Ghilardelli, G. Lini, A. Piazzi, “Path generation using eta4-splines for a truck and trailer vehicle,” Dipartimento di Ingegneria dell’Informazione, Università di Parma, Italy, Technical report TSC01-11, May 2011.

Lecture Notes

[1L] A. Piazzi, Appunti di Teoria dei Sistemi, dispensa del corso di Teoria dei Sistemi, Corso di Laurea in Ingegneria Informatica, Università degli Studi di Parma, a.a. 1998/99.
[2L] A. Piazzi, Controlli Automatici A: lucidi delle lezioni, UniNova, Parma, 2004.

Thesis

• A. Piazzi, “Sintesi di Dispositivi di Controllo che Realizzano l’Insensibilità ai Disturbi”, Laurea Thesis in Nuclear Engineering, University of Bologna (Italy), March 1982.
• A. Piazzi, “A New Geometric Approach to Basic Problems of Linear System Theory”, PhD Thesis in Systems Engineering, Department of Electronics, Computer Science and Systems, University of Bologna (Italy), December 1986.

Completion accademic year: 2019/2020

Completion accademic year: 2018/2019

Completion accademic year: 2017/2018

Completion accademic year: 2016/2017

Completion accademic year: 2015/2016

Completion accademic year: 2014/2015

Completion accademic year: 2013/2014

Professor/Teacher

Publications

Contacts

Phone number
905733
Fax number

905723