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研究一类包含不确定项和未知死区特性的严格反馈系统跟踪控制问题.首先,设计状态观测器估计不可测量的系统状态;然后,利用RBF神经网络逼近未知的系统动态;最后,基于Backstepping技术构造自适应神经网络输出反馈控制器,并减少更新参数以减轻运算负荷.所提出的控制器可以保证闭环系统中所有信号半全局最终一致有界,跟踪误差能收敛到零值小的领域内.两个仿真例子进一步验证了所提出方法的有效性.
A class of tracking control problem for a strictly feedback system with uncertainties and unknown dead zones is studied. Firstly, the state observer is designed to estimate the unmeasured state of the system. Then, the unknown system dynamics is approximated by RBF neural network. Finally, based on the Backstepping technique The adaptive neural network output feedback controller is constructed and the updating parameters are reduced to reduce the computational load.The proposed controller can ensure that all the signals in the closed-loop system eventually become uniformly bounded and the tracking error converges to a small value. Two simulation examples further verify the effectiveness of the proposed method.