Adaptive Neural Tracking Control for a Class of Nonlinear Systems With Dynamic Uncertainties
针对带有未建模动态和动态扰动的非下三角非线性系统，提出一种自适应神经控制方法，利用动态信号和变量划分技术处理设计难点，确保闭环系统信号有界且输出收敛到给定轨迹的小邻域。
This paper considers the problem of adaptive neural control of nonlower triangular nonlinear systems with unmodeled dynamics and dynamic disturbances. The design difficulties appeared in the unmodeled dynamics and nonlower triangular form are handled with a dynamic signal and a variable partition technique for the nonlinear functions of all state variables, respectively. It is shown that the proposed controller is able to ensure the semi-global boundedness of all signals of the resulting closed-loop system. Furthermore, the system output is ensured to converge to a small domain of the given trajectories. The main advantage about this research is that a neural networks-based tracking control method is developed for uncertain nonlinear systems with unmodeled dynamics and nonlower triangular form. Simulation results demonstrate the feasibility of the newly presented design techniques.