Title :
Model reference adaptive control of nonlinear systems using RLLM networks
Author :
Ali, A. ; Ashfaq, M. ; Schmid, Chr.
Author_Institution :
Dept. of Electr. Eng., Ruhr-Univ., Bochum, Germany
Abstract :
A model reference adaptive controller for nonlinear systems is implemented using rectangular local linear model (RLLM) network approach. An RLLM network is used to identify the plant as NARX model. Another network of the same kind consisting of linear controllers is tuned to achieve the closed-loop behaviour of a linear reference model. A master slave configuration is realised to decide the activation of candidate linear controllers in the controller network. The implemented control scheme is tested in simulations and real-time control on a hydraulic positioning system
Keywords :
MIMO systems; adaptive control; autoregressive processes; closed loop systems; hydraulic control equipment; identification; learning (artificial intelligence); model reference adaptive control systems; neural nets; nonlinear systems; MIMO systems; NARX model; adaptive control; closed-loop systems; hydraulic positioning system; identification; learning algorithm; master slave configuration; model reference adaptive control; neural networls; nonlinear systems; real-time systems; rectangular local linear model network; Adaptive control; Artificial neural networks; Control engineering; Control system synthesis; Linear approximation; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Vectors;
Conference_Titel :
American Control Conference, 2000. Proceedings of the 2000
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-5519-9
DOI :
10.1109/ACC.2000.879483
