状态观测的未知死区非线性系统的自适应神经网络跟踪控制

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.

Observed-based adaptive neural tracking control for nonlinear systems with unknown dead-zone

This paper deals with the problem concerned with tracking control for a class of the uncertain strict-feedback nonlinear systems with unkown dead-zone. Firstly, the state observer is established for estimating the unmeasured states. Then, by employing the radial basis function neural networks(RBF NNs), the unknown functions are approximated. Finally, the Backstepping technique is utilized to construct an adaptive neural output feedback control scheme.The designed controller decreases the number of learning parameters, and thus reduces the computational burden.It is shown that the designed neural output-feedback controller can ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded(SGUUB), and the tracking error converges to a small neighborhood of the origin. Two examples are presented to illustrate the effectiveness of the proposed approach.