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<p>In this paper, the adaptive control issue for nonlinear systems with state-dependent constraint is investigated. The state-dependent constraint is not only related to time but also related to the historical state information of the system. It is a new type of complex constraint. Moreover, the input quantization and unknown system dynamics are also considered here, which are solved by using radial basis function neural networks. Then, an adaptive controller is proposed by using the backstepping method and the barrier Lyapunov function approach such that all signals in the resulted system are bounded, all system states satisfy their corresponding state-dependent constraint condition and the system output tracks the desired tracking signal. Finally, a simulation example is employed to further show the effectiveness of the proposed approach.</p>

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Learning-based adaptive control and analysis for nonlinear systems with state-dependent constraint

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Guangshi Li

摘要

In this paper, the adaptive control issue for nonlinear systems with state-dependent constraint is investigated. The state-dependent constraint is not only related to time but also related to the historical state information of the system. It is a new type of complex constraint. Moreover, the input quantization and unknown system dynamics are also considered here, which are solved by using radial basis function neural networks. Then, an adaptive controller is proposed by using the backstepping method and the barrier Lyapunov function approach such that all signals in the resulted system are bounded, all system states satisfy their corresponding state-dependent constraint condition and the system output tracks the desired tracking signal. Finally, a simulation example is employed to further show the effectiveness of the proposed approach.