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A stable adaptive neural-network-based scheme for dynamical system control |
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| Authors: | X. Xu Y.C. Liang H.P. Lee W.Z. Lin X.H. Shi |
| Affiliation: | a College of Mathematics, Jilin University, 10 Qian Wei Road, Changchun 130012, People's Republic of China b Institute of Vibration Engineering Research, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China c College of Computer Science and Technology, Jilin University, 10 Qian Wei Road, Changchun 130012, People's Republic of China d Institute of High Performance Computing, 1 Science Park Road, #01-01 The Capricorn, Singapore Science Park II 117528, Singapore e Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1 119260, Singapore |
| Abstract: | A stable adaptive neural-network-based control scheme for dynamical systems is presented and a continuous recurrent neural network model of dynamical systems is constructed in this paper. A novel algorithm for updating weights in the neural network, which is not derived from the conventional back propagation algorithm, is also constructed. The proposed control law is obtained adaptively by a continuous recurrent neural network identifier, but not by a conventional neural network controller. In such a way, the stability in the sense of the Lyapunov stability can be guaranteed theoretically. The control error converges to a range near the zero point and remains within the domain throughout the course of the execution. Numerical experiments for a longitudinal vibration ultrasonic motor show that the proposed control scheme has good control performance. |
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