Predictor-Based Fuzzy Optimal Tracking Control With Enhanced Transient Estimation and Learning Performance for Nonlinear Systems

In this article, a finite-time learning-based optimal tracking problem for nonlinear systems with preassigned performance constraint is investigated. By designing a state predictor, a fuzzy approximator driven by prediction errors rather than tracking errors is formulated to precisely compensate the effect of the unknown uncertainties. The design realizes a decoupling of control and estimation loops, effectively ensuring transient approximation performance and avoiding chattering induced by nonzero initial tracking errors. Then, based on the estimated components, a robust steady-state control scheme embedded with a prescribed performance mechanism is tailored to guarantee that the output state can converge to a predefined range within a preassigned time. This endows the designed controller with a specified time tracking capability independence on control parameters. To make a tradeoff between tracking precision and energy cost, a finite-time learning-based optimal control policy is exploited by utilizing adaptive dynamic programming technique to serve as an adaptive supplementary controller, where single critic neural network is trained for acquiring the solution of the Hamilton–Jacobi–Bellman equation. Compared with the traditional gradient descent method, the established learning law is updated by introducing an auxiliary variable, which enhances learning performance and guarantees finite-time convergence of adaptive weights. Simulation examples examine the effectiveness and superiority of the suggested scheme.