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<p>Most of the existing results assume that the saturation function is symmetric about the origin, while in many practical situations, saturation’s asymmetric nature is a highly common occurrence. Then, according to Lyapunov stability theory, Schur complement, matrix inequality and sector nonlinearity model approach, this paper studies the challenge of achieving intermittent stabilization in uncertain nonlinear systems with asymmetric actuator saturation. Sufficient conditions for the stabilization of a specific category of nonlinear systems subject to uncertain parameters are derived. Besides, for the purpose of simplifying the process of finding suitable control gains to stabilize the system, we also study the design of control gain and put forward the LMI optimization problem with the aim of obtaining the larger estimation of the domain of attraction (DOA). Finally, numerical simulation about the exponential stabilization of neural network with uncertain parameters verifies the effectiveness and practicability of our proposed control method.</p>

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Intermittent Stabilization of Uncertain Nonlinear Systems with Asymmetric Actuator Saturation

  • 期刊论文

  • 发表日期：

    2025年10月3日

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  • Hongjuan Wu,
  • Lin Huang,
  • Dongfang Yan,
  • Jie Shen,
  • Qiangqiang Zhang,
  • Zhengwen Tu

摘要

Most of the existing results assume that the saturation function is symmetric about the origin, while in many practical situations, saturation’s asymmetric nature is a highly common occurrence. Then, according to Lyapunov stability theory, Schur complement, matrix inequality and sector nonlinearity model approach, this paper studies the challenge of achieving intermittent stabilization in uncertain nonlinear systems with asymmetric actuator saturation. Sufficient conditions for the stabilization of a specific category of nonlinear systems subject to uncertain parameters are derived. Besides, for the purpose of simplifying the process of finding suitable control gains to stabilize the system, we also study the design of control gain and put forward the LMI optimization problem with the aim of obtaining the larger estimation of the domain of attraction (DOA). Finally, numerical simulation about the exponential stabilization of neural network with uncertain parameters verifies the effectiveness and practicability of our proposed control method.