Title :
Use of a recurrent neural network in discrete sliding-mode control
Author :
Fang, Y. ; Chow, T.W.S. ; Li, X.-D.
Author_Institution :
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, Hong Kong
fDate :
1/1/1999 12:00:00 AM
Abstract :
Discusses a class of nonlinear discrete sliding-mode control. The control system is designed on the basis of a discrete Lyapunov function. Part of the equivalent control is estimated by an online estimator, which is realised by a recurrent neural network (RNN) because of its outstanding ability for modelling a dynamical process. A real-time iterative learning algorithm is developed and used to train the RNN. Unlike the conventional learning algorithms for RNNs, the proposed algorithm ensures that the learning error converges to zero. As a result, the stability of the control system is always assured. In addition, this learning algorithm can be applied for online estimation. The proposed controller eliminates chattering and provides sliding-mode motion on the selected manifolds in the state space. Numerical examples are given and simulation results strongly demonstrate that the control scheme is very effective
Keywords :
Lyapunov methods; control system synthesis; discrete time systems; learning (artificial intelligence); neurocontrollers; nonlinear control systems; recurrent neural nets; stability; variable structure systems; chattering elimination; discrete Lyapunov function; dynamical process; nonlinear discrete sliding-mode control; online estimator; real-time iterative learning algorithm;
Journal_Title :
Control Theory and Applications, IEE Proceedings -
DOI :
10.1049/ip-cta:19990376