Switching supervisory control based on controller falsification and closed-loop performance inference

Abstract The paper addresses two of the basic issues of switching supervisory control (SSC): controller falsification (CF) and inference of candidate loop behaviour (ICLB). CF is approached as a statistical fault detection problem in that the currently operating controller is falsified as soon as a divergence trend is detected. This is achieved by considering a statistic (or residual) in the form of a ratio of closed-loop variables, and the falsification test is carried out by comparing at each time the ratio statistic with a threshold. It is constructively shown that the thresholds can be fixed, irrespective of the disturbance intensity, in such a way that faults are detected with probability one while probability of false alarms can be made as small as we wish. The ICLB issue is approached by the virtual reference approach. This allows one to obtain an inference of the performance of a candidate loop via a mean-square average of suitably filtered prediction errors. It is shown how a supervisory logic can be built by combining the results on CF with those on ICLB.

[1]  Edoardo Mosca,et al.  Closed-loop performance inference for controller selection in switching supervisory control , 1999, 1999 European Control Conference (ECC).

[2]  Karl Johan Åström,et al.  Computer-Controlled Systems: Theory and Design , 1984 .

[3]  Qiang Liu,et al.  Robust H-infinity control synthesis method and its application to benchmark problems , 1992 .

[4]  Job van Amerongen,et al.  A supervisor for control of mode-switch processes , 1994, Autom..

[5]  S. M. Savaresi,et al.  Data-based simultaneous design of composite feedback-feedforward controllers: A virtual input direct design approach , 1997, 1997 European Control Conference (ECC).

[6]  Manfred Morari,et al.  Robust constrained model predictive control using linear matrix inequalities , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[7]  A. Morse.  Supervisory control of families of linear set-point controllers Part I. Exact matching , 1996, IEEE Trans. Autom. Control..

[8]  Robert L. Kosut.  Uncertainty Model Unfalsification for Robust Adaptive Control , 1998 .

[9]  Edoardo Mosca,et al.  H∞-Control Theory , 1991 .

[10]  E. Mosca.  Optimal, Predictive and Adaptive Control , 1994 .

[11]  P. Ramadge,et al.  Model and controller selection policies based on output prediction errors , 1996, IEEE Trans. Autom. Control..

[12]  H. Kwakernaak.  The polynomial approach to H ???-optimal regulation , 1991 .

[13]  Bong Wie,et al.  Robust H ∞ Control Synthesis Method and Its Application to a Benchmark Problem , 1991 .

[14]  Fredrik Gustafsson,et al.  Closed-Loop Performance Monitoring in the Presence of System Changes and Disturbances , 1998, Autom..

[15]  Kumpati S. Narendra,et al.  Adaptive control using multiple models , 1997, IEEE Trans. Autom. Control..

[16]  Michael G. Safonov,et al.  The unfalsified control concept and learning , 1997 .