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

作者：Si, Wen-Jie^*; Wang, Cong; Dong, Xun-De; Zeng, Wei

来源：Control and Decision, 2017, 32(9): 1537-1546.

DOI：10.13195/j.kzyjc.2016.0960

• Adaptive neural control
• Barrier lyapunov functions
• Lyapunov Krasovskii function
• Nonlinear time delay systems
• Output feedback
• Radial basis function neural networks
• Semi-globally uniformly ultimately bounded
• Single input and single outputs

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.

• 出版日期2017-9-1
• 单位华南理工大学