全状态约束的时滞系统神经网络输出反馈控制

Neural output-feedback control for time-delay systems with full-state constraints

针对一类严格反馈形式的单输入单输出时滞系统,研究在全状态约束下的输出反馈控制.首先,设计状态观测器估计不可测量的状态;其次,利用RBF神经网络逼近未知的非线性函数,利用障碍Lyapunov函数确保全状态约束及Lyapunov-Krasovskii方法消除时滞对系统的影响;最后,设计输出反馈控制器,并且有更少的更新参数减少了计算负荷.所设计的控制器可以保证闭环系统中所有信号半全局一致最终有界,信号误差收敛到小的领域内.仿真例子进一步验证了所提出方法的有效性.

This paper deals with the problem concerned with tracking control for a class of single input and single output（SISO） strict-feedback nonlinear time-delay systems with full-state constraints. Firstly, the state observer is designed for estimating the unmeasured states. Then, by employing the Radial basis function neural networks（RBF NNs）, the unknown functions are approximated. Meanwhile, a barrier Lyapunov function is utilized to ensure that the output parameters are restricted and the effects of unknown time-delays are eliminated by choosing appropriate Lyapunov- Krasovskii functions in the design procedure. Finally, an output feedback control scheme is constructed and less learning parameters are used in barrier Lyapunov function backstepping design, and thus reduce the computational burden. It is shown that the designed 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. An example is presented to illustrate the effectiveness of the proposed method.