A fading memory data-driven algorithm for controller switching

A data-driven controller switching algorithm used for adaptive control is investigated. A new cost-detectable cost function based on fading memory data is constructed so as to reduce the influence of older data. A new controller switching algorithm is designed to guarantee that switching stops and that the closed-loop system is stable. Theoretical analyses and simulations are presented to show that, when the plant changes slowly or infrequently, the new algorithm can detect instability and switch to a stabilizing controller sooner and more smoothly, once the currently active controller becomes destabilizing for the new plant dynamics. It is also shown that this algorithm can be used to attenuate the Dehghani-Anderson-Lanzon phenomenon.

[1]  Giorgio Battistelli,et al.  Stability of Unfalsified Adaptive Switching Control in Noisy Environments , 2010, IEEE Transactions on Automatic Control.

[2]  João Pedro Hespanha,et al.  Overcoming the limitations of adaptive control by means of logic-based switching , 2003, Syst. Control. Lett..

[3]  Maarten Steinbuch,et al.  Data-driven multivariable controller design using Ellipsoidal Unfalsified Control , 2007, 2007 American Control Conference.

[4]  A. Morse,et al.  Applications of hysteresis switching in parameter adaptive control , 1992 .

[5]  Michael Safonov,et al.  Unfalsified Adaptive Control: The Benefit of Bandpass Filters , 2008 .

[6]  Håkan Hjalmarsson,et al.  Iterative feedback tuning—an overview , 2002 .

[7]  Daniel Liberzon,et al.  Supervisory Control of Uncertain Linear Time-Varying Systems , 2011, IEEE Transactions on Automatic Control.

[8]  Giorgio Battistelli,et al.  Multi-model unfalsified adaptive switching supervisory control , 2010, Autom..

[9]  Giorgio Battistelli,et al.  Unfalsified adaptive switching supervisory control of time varying systems , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[10]  Ari Ingimundarson,et al.  Using the Unfalsified Control Concept to achieve Fault Tolerance , 2008 .

[11]  Brian D. O. Anderson,et al.  Challenges of adaptive control-past, permanent and future , 2008, Annu. Rev. Control..

[12]  Urbashi Mitra,et al.  Adaptive power control for wireless networks using multiple controllers and switching , 2004, IEEE Transactions on Neural Networks.

[13]  Michael G. Safonov,et al.  The unfalsified control concept and learning , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[14]  Brian D. O. Anderson,et al.  Unfalsified adaptive control: A new controller implementation and some remarks , 2007, 2007 European Control Conference (ECC).

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

[16]  Michael J. Grimble,et al.  Iterative Learning Control for Deterministic Systems , 1992 .

[17]  A. Paul,et al.  Cost-detectability and Stability of Adaptive Control Systems , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[18]  Michael G. Safonov,et al.  Safe Adaptive Switching Control: Stability and Convergence , 2008, IEEE Transactions on Automatic Control.