Fixed-Time Adaptive Neural Network Tracking Control for Output-Constrained High-Order Systems Using Command Filtered Strategy [post]

Lian Chen, Qing Wang
2021 unpublished
This paper proposes a fixed time adaptive neural command filtered controller for a category of high-order systems based on adding a power integrator technique. Different from existing research, the presented controller has the following distinguishing advantages: i) fixed-time control framework is extended to the tracking control problem of high-order systems. ii) the error compensation mechanism eliminates filter errors that arise from dynamic controllers. iii) growth assumptions about unknown
more » ... functions are relaxed with the help of adaptive neural networks. iv) more general systems: the developed controller can apply to high-order systems subject to uncertain dynamics, unknown gain functions and asymmetric constraints. Stability analysis shows that all states are semi-globally bounded, and the convergence rate of tracking error is independent of initial conditions. The main innovation of our work is that the presented controller considers simultaneously filter errors, fixed-time convergence, growth conditions and asymmetric output constraint for the tracking control issue of high-order systems. Finally, the simulation results validate the advantages and efficacy of the developed control scheme.
doi:10.21203/ fatcat:47dk5rcn2jar5e3lnutqb5f5pq