Practical Prescribed-Time Guaranteed Performance Control for Pure-Feedback Systems Without Accurate Initial Errors
Transient behavior,steady-state precision,regulation time,and conver-gence rate are the four key indicators for evaluating closed-loop control system per-formance.In this paper,the authors propose a tracking control design scheme that simultaneously satisfies the above four indicators for unmatched uncertain pure feed-back nonlinear systems.This scheme ensures that the system output signal always stays within the envelope range formed by the performance function by setting the performance function.At the same time,under a novel error transformation mecha-nism control,the steady-state precision and regulation time of the closed-loop system can be pre-set.The authors use neural networks to approximate completely unknown nonlinear functions,where the weights of the neural network can be updated online by adaptive laws.In addition,the authors add the σ-correction term in the adaptive law to avoid parameter estimation drift phenomenon.Finally,simulation results val-idate the effectiveness of the proposed control method and its superiority in control performance.
Adaptive controlRBFNNpure-feedback systemsnonlinear systems
万方数据
维普
