This paper presents an improved backstepping control implementation scheme for a n-dimensional strict-feedback uncertain nonlinear system based on command filtered backstepping and adaptive neural network backstepping. In this approach, n command filters and one neural network are applied to reconstruct the approximations of unknown nonlinearities, which are related to the system uncertainties including the system's unmodeled dynamics and external disturbances. Then, one can use the negative feedback of these approximations to compensate the system uncertainties. Moreover, convex optimization and soft computing technique are adopted to design the update law of the weights of the neural network, and Lyapunov stability criterion is used to prove the stability of the closed-loop system. Finally, simulation results are given to show the effectiveness of the proposed methods.

中文翻译：

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基于命令滤波器和通用逼近器的不确定性严格反馈非线性系统的反步控制设计


针对n维严格反馈不确定非线性系统，提出一种基于命令滤波反步和自适应神经网络反步的改进反步控制实现方案。在该方法中，应用n个命令滤波器和一个神经网络来重建未知非线性的近似值，这些非线性与系统不确定性（包括系统的未建模动态和外部扰动）相关。然后，可以利用这些近似值的负反馈来补偿系统的不确定性。此外，采用凸优化和软计算技术设计了神经网络权值的更新规律，并利用Lyapunov稳定性判据证明了闭环系统的稳定性。最后，给出了仿真结果，证明了所提方法的有效性。