Resumen de Data-based reinforcement learning approximate optimal control for an uncertain nonlinear system with control effectiveness faults
Patryk Deptula, Zachary I. Bell, Emily A. Doucette, J. Willard Curtis, W.E. Dixon
An infinite horizon approximate optimal control problem is developed for a system with unknown drift parameters and control effectiveness faults. A data-based filtered parameter estimator with a novel dynamic gain structure is developed to simultaneously estimate the unknown drift dynamics and control effectiveness fault. A local state-following approximate dynamic programming method is used to approximate the unknown optimal value function for an uncertain system. Using a relaxed persistence of excitation condition, a Lyapunov-based stability analysis shows exponential convergence to a residual error for the parameter estimation and uniformly ultimately bounded convergence for the closed-loop system. Simulation results are presented which demonstrate the effectiveness of the developed method.
