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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori
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Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori

机译：具有不确定跟踪精度的不确定严格反馈系统的全局稳定自适应Backstepping神经网络控制先验

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摘要

This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given . In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat’s Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately , and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.
机译：本文在给出最终跟踪误差精度的前提下，针对一类不确定的严格反馈系统，解决了全局稳定的直接自适应反步神经网络（NN）跟踪控制设计问题。与经典的自适应反推神经网络控制方案相比，本文通过一些非负函数而非正定Lyapunov函数，使用Barbalat引理分析了跟踪误差的收敛性。因此，可以精确地确定和调整最终跟踪误差的精度，并确保闭环系统最终全局统一有界。主要的技术新颖之处在于构造三个新的三阶连续可微函数，用于设计控制律，虚拟控制变量和自适应律。最后，给出了两个仿真例子来说明所提控制方法的有效性和优势。

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