Adaptive Observer-Based Inverse Optimal Control of a Class of Second-Order Euler-Lagrange Systems

Abstract

An adaptive observer-based Inverse optimal controller (AOC) is proposed for a class of second-order Euler-Lagrange systems with various uncertainties in the dynamic models. Specifically, the proposed AOC adopts one NN-based robust adaptive inverse optimal controller to approximate the nonlinear unknown system and generate optimal control inputs, while the other NN-based adaptive observer is established to estimate the unmeasured system state. The developed AOC is proved to achieve semi-global asymptotic optimal tracking (by inverse optimal controller) through Lyapunov stability analysis. Simulation analysis shows that the AOC has small tracking error even with the observed information in the presence of uncertain disturbances.