Online Adaptive Critic Learning Control of Unknown Dynamics With Application to Deep Submergence Rescue Vehicle

As a powerful tool for nonlinear systems robust controller design, robust adaptive dynamic programming (RADP) methods require initial admissible control and prior knowledge of disturbance to be effective. As the most effective approach to provide robustness to uncertainties, active disturbance attenuation (ADA), was rarely considered in RADP literatures. To combine ADA with RADP, a neural-network identifier was developed initially to approximate the plant dynamics and the imposed external disturbance. System states was extended with the approximated disturbance to establish ADA actor-critic learning. To relax the initial admissible control constraint, a novel auxiliary system were created based on the identifier dynamics. Theoretical analysis and simulations on unstable nonlinear system show that the approximated control law with respect to the auxiliary system and a newly proposed cost function itself could guarantee asymptotic stability of the original system. Simulations and comparison with other model-free control techniques demonstrated the excellent performance and robustness of the proposed method. Applicability of the proposed method was validated by applying it to trajectory tracking control of a deep submergence rescue vehicle.