Iterative Feedback and Learning Control. Servo systems applications

Abstract The paper presents aspects concerning two iterative methods in control design: the Iterative Feedback Tuning (IFT) and the Iterative Learning Control (ILC) approaches. The IFT is focused on a new generalized formulation that minimizes an objective function resulting in both controller and reference model tuning. The ILC is expressed as several structures including the original simplified norm-optimal ILC. The theoretical results are validated in case of PI controllers for servo systems with second-order integral type controlled plants and real-time experiments are included. The combination between IFT or ILC and fuzzy control is successful in applications leading to performance enhancement.

[1]  Robert Babuska,et al.  Perspectives of fuzzy systems and control , 2005, Fuzzy Sets Syst..

[2]  K. Moore,et al.  Algebraic $H_infty$ Design of Higher-Order Iterative Learning Controllers , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..

[3]  David H. Owens,et al.  Iterative learning control - An optimization paradigm , 2015, Annu. Rev. Control..

[4]  Svante Gunnarsson,et al.  Iterative feedback tuning: theory and applications , 1998 .

[5]  Samer S. Saab.  Optimality of first-order ILC among higher order ILC , 2006, IEEE Transactions on Automatic Control.

[6]  Tore Hägglund,et al.  Benchmark systems for PID control , 2000 .

[7]  Stefan Preitl,et al.  Low cost fuzzy controlled servo systems in mechatronic systems , 2006 .

[8]  Kenzo Nonami,et al.  Optimal two-degree-of-freedom fuzzy control for locomotion control of a hydraulically actuated hexapod robot , 2007, Inf. Sci..

[9]  Sergio M. Savaresi,et al.  Virtual reference feedback tuning for two degree of freedom controllers , 2002 .

[10]  Sang-Min Kim,et al.  Induction motor servo drive using robust PID-like neuro-fuzzy controller , 2006 .

[11]  Magnus Mossberg,et al.  Iterative feedback tuning of PID parameters: comparison with classical tuning rules , 2003 .

[12]  László T. Kóczy,et al.  Application of interpolation-based fuzzy logic reasoning in behaviour-based control structures , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[13]  Sergio M. Savaresi,et al.  Virtual reference feedback tuning: a direct method for the design of feedback controllers , 2002, Autom..

[14]  Denis Dochain,et al.  Monitoring and control of process and poxer systems : towards new paradigms , 2005 .

[15]  N. Adachi,et al.  Iterative Learning Control Using Adjoint Systems and Stable Inversion , 2002 .

[16]  S. Gunnarsson,et al.  A convergent iterative restricted complexity control design scheme , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[17]  Yeung Yam,et al.  Determination of Different Polytopic Models of the Prototypical Aeroelastic Wing Section by TP Model Transformation , 2006, J. Adv. Comput. Intell. Intell. Informatics.

[18]  A.G. Alleyne,et al.  A survey of iterative learning control , 2006, IEEE Control Systems.

[19]  Stefan Preitl,et al.  Fuzzy Controllers With Maximum Sensitivity for Servosystems , 2007, IEEE Transactions on Industrial Electronics.

[20]  Guoping Liu,et al.  Event-Driven Networked Predictive Control , 2007, IEEE Transactions on Industrial Electronics.

[21]  O.P. Malik,et al.  Multivariable Adaptive Control of Synchronous Machines in a Multimachine Power System , 2006, IEEE Transactions on Power Systems.

[22]  S. Saab.  Stochastic P-type/D-type iterative learning control algorithms , 2003 .

[23]  Kemao Peng,et al.  Robust Composite Nonlinear Feedback Control With Application to a Servo Positioning System , 2007, IEEE Transactions on Industrial Electronics.

[24]  Malcolm Irving,et al.  Integration of algorithmic and heuristic techniques for transition-optimised voltage and reactive power control , 2006 .

[25]  Sergio M. Savaresi,et al.  Direct nonlinear control design: the virtual reference feedback tuning (VRFT) approach , 2006, IEEE Transactions on Automatic Control.

[26]  Stefan Preitl,et al.  An extension of tuning relations after symmetrical optimum method for PI and PID controllers , 1999, Autom..

[27]  Okko H. Bosgra,et al.  Convergence design considerations of low order Q-ILC for closed loop systems, implemented on a high precision wafer stage , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[28]  T. Fukuda,et al.  Iterative feedback tuning of controllers for a two-mass-spring system with friction , 2003 .

[29]  R. Precup,et al.  Genetic Iterative Feedback Tuning (GIFT) Method for Fuzzy Control System Development , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[30]  Richard W. Longman,et al.  Simple learning control made practical by zero-phase filtering: applications to robotics , 2002 .

[31]  Xiang Gao,et al.  Design study of an adaptive Fuzzy-PD controller for pneumatic servo system , 2005 .

[32]  Toshiharu Sugie,et al.  Iterative learning control of Hamiltonian systems: I/O based optimal control approach , 2003, IEEE Trans. Autom. Control..

[33]  T. Sogo.  Stable inversion for nonminimum phase sampled-data systems and its relation with the continuous-time counterpart , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[34]  Kaoru Hirota,et al.  On Approximation Capability of Pseudo-linear Shepard Approximation Operators , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[35]  M. Saad,et al.  Identification and Real-Time Control of an Electrohydraulic Servo System Based on Nonlinear Backstepping , 2007, IEEE/ASME Transactions on Mechatronics.