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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Peaking-Free Output-Feedback Adaptive Neural Control Under a Nonseparation Principle
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Peaking-Free Output-Feedback Adaptive Neural Control Under a Nonseparation Principle

机译：非分离原理下的无峰输出反馈自适应神经控制

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摘要

High-gain observers have been extensively applied to construct output-feedback adaptive neural control (ANC) for a class of feedback linearizable uncertain nonlinear systems under a nonlinear separation principle. Yet due to static-gain and linear properties, high-gain observers are usually subject to peaking responses and noise sensitivity. Existing adaptive neural network (NN) observers cannot effectively relax the limitations of high-gain observers. This paper presents an output-feedback indirect ANC strategy under a nonseparation principle, where a hybrid estimation scheme that integrates an adaptive NN observer with state variable filters is proposed to estimate plant states. By applying a single Lyapunov function candidate to the entire system, it is proved that the closed-loop system achieves practical asymptotic stability under a relatively low observer gain dominated by controller parameters. Our approach can completely avoid peaking responses without control saturation while keeping favourable noise rejection ability. Simulation results have shown effectiveness and superiority of this approach.
机译：高增益观测器已被广泛应用于在非线性分离原理下为一类反馈线性化不确定非线性系统构造输出反馈自适应神经控制（ANC）。然而，由于静态增益和线性特性，高增益观察者通常会遇到峰值响应和噪声敏感性。现有的自适应神经网络（NN）观察者无法有效地放宽高增益观察者的限制。本文提出了一种基于非分离原理的输出反馈间接ANC策略，其中提出了一种将自适应NN观测器与状态变量滤波器相集成的混合估计方案来估计工厂状态。通过将单个李雅普诺夫函数候选者应用于整个系统，证明了闭环系统在以控制器参数为主导的相对较低的观察者增益下实现了实用的渐近稳定性。我们的方法可以完全避免峰值响应，而又不会产生饱和控制，同时保持良好的噪声抑制能力。仿真结果表明了该方法的有效性和优越性。

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