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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Adaptive Neural PD Control With Semiglobal Asymptotic Stabilization Guarantee
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Adaptive Neural PD Control With Semiglobal Asymptotic Stabilization Guarantee

机译：具有半全局渐近稳定保证的自适应神经PD控制

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摘要

This paper proves that adaptive neural plus proportional-derivative (PD) control can lead to semiglobal asymptotic stabilization rather than uniform ultimate boundedness for a class of uncertain affine nonlinear systems. An integral Lyapunov function-based ideal control law is introduced to avoid the control singularity problem. A variable-gain PD control term without the knowledge of plant bounds is presented to semiglobally stabilize the closed-loop system. Based on a linearly parameterized raised-cosine radial basis function neural network, a key property of optimal approximation is exploited to facilitate stability analysis. It is proved that the closed-loop system achieves semiglobal asymptotic stability by the appropriate choice of control parameters. Compared with previous adaptive approximation-based semiglobal or asymptotic stabilization approaches, our approach not only significantly simplifies control design, but also relaxes constraint conditions on the plant. Two illustrative examples have been provided to verify the theoretical results.
机译：本文证明，对于一类不确定仿射非线性系统，自适应神经加比例微分（PD）控制可导致半全局渐近稳定，而不是一致的最终有界性。引入了基于李雅普诺夫函数积分的理想控制律，以避免控制奇异性问题。提出了一种不带植物界线的变增益PD控制项，以半全局稳定闭环系统。基于线性参数化的升高余弦径向基函数神经网络，利用最佳逼近的关键属性来促进稳定性分析。证明了通过适当选择控制参数，闭环系统达到了半全局渐近稳定性。与以前的基于自适应逼近的半全局或渐近稳定方法相比，我们的方法不仅显着简化了控制设计，而且还放宽了工厂的约束条件。提供了两个说明性的例子来验证理论结果。

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