Pure Feedback Control for Nonlinear Systems Based on Predefined-Time Performance Function
Yang Li, Yaqi Yu, Quanmin Zhu, Jianhua Zhang, Cheng Siong Chin
Source abstract
This paper investigates the predefined-time adaptive neural tracking control problem for a class of nonlinear pure feedback systems with full state constraints. A novel barrier Lyapunov function (BLF) integrated with a predefined-time performance function (PTPF) is constructed to ensure that the tracking error converges to a prescribed residual set within a user-specified time, while strictly enforcing the state constraints throughout the entire operation. To overcome the “complexity explosion” inherent in the traditional backstepping design, a predefined-time-dynamic surface filter is introduced, whose convergence time can be arbitrarily prescribed independently of initial conditions. Radial basis function neural networks (RBF NNs) are employed to approximate unknown nonlinear dynamics, and adaptive laws are derived to update the weight estimates online. By means of a composite Lyapunov function, it is proven that all closed loop signals are predefined-time-bounded and the tracking error meets the preset performance specifications. Simulation results on the Brusselator oscillator model demonstrate the effectiveness and superiority of the proposed scheme, showing rapid convergence, strict constraint satisfaction, and good robustness against external disturbances.
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