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Direct adaptive output tracking control using multilayered neural networks

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3 Author(s)
Jin, L. ; Coll. of Eng., Saskatchewan Univ., Saskatoon, Sask., Canada ; Nikiforuk, P.N. ; Gupta, M.M.

Multilayered neural networks are used to construct nonlinear learning control systems for a class of unknown nonlinear systems in a canonical form. An adaptive output tracking architecture is proposed using the outputs of the two three-layered neural networks which are trained to approximate the unknown nonlinear plant to any desired degree of accuracy by using the modified back-propagation technique. A weight-learning algorithm is presented using the gradient descent method with a dead-zone function, and the descent and convergence of the error index during weight learning are shown. The closed-loop system is proved to be stable, with the output tracking error converging to the neighbourhood of the origin. The effectiveness of the proposed control scheme is illustrated through simulations.

Published in:

Control Theory and Applications, IEE Proceedings D  (Volume:140 ,  Issue: 6 )