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首页> 外文期刊>International Journal of Robust and Nonlinear Control >Observer-based decentralized adaptive neural control for uncertain interconnected systems with input quantization and time-varying output constraints
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Observer-based decentralized adaptive neural control for uncertain interconnected systems with input quantization and time-varying output constraints

机译：基于观察者的分散自适应神经控制，用于输入量化的不确定互连系统和时变输出约束

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摘要

In this article, observer-based decentralized adaptive neural control is proposed for uncertain interconnected nonlinear systems with input quantization and time-varying output restrictions. Decentralized hysteresis quantizer is employed to handle input signal. The unmeasured states are estimated by designing K-filters. A Lyapunov description is used to dispose of state unmodeled dynamics. The constrained interconnected nonlinear systems are transformed into novel interconnected nonlinear systems without output constraints by constructing invertible nonlinear mappings. Dynamic surface control technique and a hyperbolic tangent function are adopted to design decentralized controller, which has a simpler structure. Stability analysis indicates that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded and system outputs satisfy the constraints. Two numerical examples are used to verify the effectiveness of the theoretical findings.
机译：在本文中，提出了基于观察者分散的自适应神经控制，用于输入量化和时变输出限制的不确定互连的非线性系统。 用于处理输入信号的分散滞后量化器。 通过设计K滤波器估计未测量状态。 Lyapunov描述用于处理状态未确定的动态。 受约束的互连非线性系统通过构造可逆的非线性映射而改变为没有输出约束的新颖互连的非线性系统。 采用动态表面控制技术和双曲线切线函数设计分散控制器，具有更简单的结构。 稳定性分析表明闭环系统中的所有信号都是半球形均匀的最终界限，系统输出满足约束。 使用两个数值例子来验证理论发现的有效性。

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