MLP-based adaptive neural control of nonlinear time-delay systems with the unknown hysteresis |
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Authors: | Guoqing Zhang Zhijian Sun Lei Qiao |
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Affiliation: | 1. Department of Automation, Shanghai Jiao Tong University, Shanghai, China;2. Navigation College, Dalian Maritime University, Dalian, China |
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Abstract: | In this note, the authors study the tracking problem for uncertain nonlinear time-delay systems with unknown non-smooth hysteresis described by the generalised Prandtl–Ishlinskii (P-I) model. A minimal learning parameters (MLP)-based adaptive neural algorithm is developed by fusion of the Lyapunov–Krasovskii functional, dynamic surface control technique and MLP approach without constructing a hysteresis inverse. Unlike the existing results, the main innovation can be summarised as that the proposed algorithm requires less knowledge of the plant and independent of the P-I hysteresis operator, i.e. the hysteresis effect is unknown for the control design. Thus, the outstanding advantage of the corresponding scheme is that the control law is with a concise form and easy to implement in practice due to less computational burden. The proposed controller guarantees that the tracking error converges to a small neighbourhood of zero and all states of the closed-loop system are stabilised. A simulation example demonstrates the effectiveness of the proposed scheme. |
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Keywords: | Adaptive neural control backstepping hysteresis time delays nonlinear system |
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