Adaptive Neural Control of MIMO Nonstrict-Feedback Nonlinear Systems With Time Delay
针对一类系数和函数未知的多输入多输出非严格反馈非线性时延系统，利用神经网络逼近未知函数并设计输入驱动滤波器，提出一种反步跟踪控制器，保证闭环信号有界且跟踪误差小。
In this paper, an adaptive neural output-feedback tracking controller is designed for a class of multiple-input and multiple-output nonstrict-feedback nonlinear systems with time delay. The system coefficient and uncertain functions of our considered systems are both unknown. By employing neural networks to approximate the unknown function entries, and constructing a new input-driven filter, a backstepping design method of tracking controller is developed for the systems under consideration. The proposed controller can guarantee that all the signals in the closed-loop systems are ultimately bounded, and the time-varying target signal can be tracked within a small error as well. The main contributions of this paper lie in that the systems under consideration are more general, and an effective design procedure of output-feedback controller is developed for the considered systems, which is more applicable in practice. Simulation results demonstrate the efficiency of the proposed algorithm.