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Terminal iterative learning control for discrete-time nonlinear systems based on neural networks |
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| Authors: | Jian Han Dong Shen Chiang-Ju Chien |
| Institution: | 1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China;2. Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam 1098XH, The Netherlands;3. Department of Electronic Engineering, Huafan University, New Taipei City 22301, Taiwan |
| Abstract: | The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms. |
| Keywords: | Corresponding author |
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