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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique
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Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique

机译：基于动态表面技术的纯反馈非线性时滞系统的自适应神经控制

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摘要

This paper is concerned with robust stabilization problem for a class of nonaffine pure-feedback systems with unknown time-delay functions and perturbed uncertainties. Novel continuous packaged functions are introduced in advance to remove unknown nonlinear terms deduced from perturbed uncertainties and unknown time-delay functions, which avoids the functions with control law to be approximated by radial basis function (RBF) neural networks. This technique combining implicit function and mean value theorems overcomes the difficulty in controlling the nonaffine pure-feedback systems. Dynamic surface control (DSC) is used to avoid “the explosion of complexity” in the backstepping design. Design difficulties from unknown time-delay functions are overcome using the function separation technique, the Lyapunov-Krasovskii functionals, and the desirable property of hyperbolic tangent functions. RBF neural networks are employed to approximate desired virtual controls and desired practical control. Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced significantly, and semiglobal uniform ultimate boundedness of all of the signals in the closed-loop system is guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed design scheme.
机译：本文涉及一类具有未知时滞函数和不确定不确定性的非仿射纯反馈系统的鲁棒镇定问题。预先介绍了新颖的连续打包函数，以消除由扰动的不确定性和未知的时间延迟函数推导的未知非线性项，从而避免了具有控制律的函数被径向基函数（RBF）神经网络近似。结合隐函数和均值定理的这项技术克服了控制非仿射纯反馈系统的困难。动态曲面控制（DSC）用于避免后推设计中的“复杂性爆炸”。使用函数分离技术，Lyapunov-Krasovskii泛函和双曲正切函数的理想特性，可以克服未知时延函数带来的设计难题。使用RBF神经网络来近似所需的虚拟控件和所需的实际控件。在提出的自适应神经DSC下，所需的自适应参数数量大大减少，并且保证了闭环系统中所有信号的半全局一致最终有界性。仿真研究表明了所提出的设计方案的有效性。

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