Title :
Model based tuning and adaption of fuzzy logic controllers
Author :
Papp, Z. ; Driessen, B.J.F.
Author_Institution :
TNO Inst. of Appl. Phys., Delft, Netherlands
Abstract :
This paper presents a fuzzy logic based control structure enhanced with supervised learning and/or adaption functionalities. Availability of at least a partial process model is assumed. Nonlinear process identification procedure is used to complete the partial model. Based on this process identification model using the techniques of systems sensitivity theory, the necessary gradients are generated to guide the training process and thus to keep the training time (the number of observations) at minimum. The process identification and the controller tuning can run in parallel, in this way the online adaption of the controller can be realized in a straightforward way. A supervisory robot control problem is shown to demonstrate the capabilities of the scheme proposed
Keywords :
fuzzy control; fuzzy logic; identification; learning (artificial intelligence); model reference adaptive control systems; robots; self-adjusting systems; sensitivity analysis; adaption functionalities; controller tuning; fuzzy logic controllers; gradients; model based tuning; nonlinear process identification; partial process model; supervised learning; supervisory robot control; systems sensitivity theory; Adaptive control; Adaptive systems; Fuzzy logic; Network synthesis; Optimal control; Physics; Programmable control; Robot control; Supervised learning; Trajectory;
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
DOI :
10.1109/FUZZY.1994.343930
