This article is concerned with the issue of adaptive prescribed performance tracking control for a class of pure-feedback nonlinear systems with full-state time-varying constraints. By using the hyperbolic tangent function as nonlinear mapping technique, the obstacle of the full-state constraints is resolved. A novel adaptive control method is proposed by prescribed performance control with command filtered backstepping design, and the insufficient of the dynamic surface method is conquered by the error compensation mechanism. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, it is shown that all signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output tracking error always converges to the prescribed performance bound. Two simulation examples demonstrate the capability of the proposed control strategy.

中文翻译：

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具有全状态时变约束的不确定纯反馈非线性系统的基于命令滤波器的自适应规定性能跟踪控制

本文关注一类具有全状态时变约束的纯反馈非线性系统的自适应规定性能跟踪控制问题。通过使用双曲正切函数作为非线性映射技术，解决了全状态约束的障碍。提出了一种基于指令滤波反步设计的规定性能控制的自适应控制方法，通过误差补偿机制克服了动态曲面法的不足。利用径向基函数神经网络来逼近未知的非线性函数。基于李雅普诺夫稳定性理论，表明闭环系统中的所有信号都是半全局一致最终有界的，输出跟踪误差总是收敛到规定的性能界限。