Command Filter-Based Adaptive Neural Tracking Controller Design for Uncertain Switched Nonlinear Output-Constrained Systems
针对一类带输出约束的不确定切换非线性系统，提出一种基于命令滤波和神经网络的自适应跟踪控制器，通过引入障碍李雅普诺夫函数将约束系统转化为无约束系统，解决反步法中的复杂度爆炸问题，并确保输出不超限且跟踪误差可任意小。
In this paper, a new adaptive approximation-based tracking controller design approach is developed for a class of uncertain nonlinear switched lower-triangular systems with an output constraint using neural networks (NNs). By introducing a novel barrier Lyapunov function (BLF), the constrained switched system is first transformed into a new system without any constraint, which means the control objectives of the both systems are equivalent. Then command filter technique is applied to solve the so-called "explosion of complexity" problem in traditional backstepping procedure, and radial basis function NNs are directly employed to model the unknown nonlinear functions. The designed controller ensures that all the closed-loop variables are ultimately boundedness, while the output limit is not transgressed and the output tracking error can be reduced arbitrarily small. Furthermore, the use of an asymmetric BLF is also explored to handle the case of asymmetric output constraint as a generalization result. Finally, the control performance of the presented control schemes is illustrated via two examples.