Abstract

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.

Highlights

  • In the past decade, adaptive dynamic programming (ADP), wherein adaptive parameter identification is combined with conventional dynamic programming to solve nonlinear system optimal control problem forward-in-time, has received increasing attention in the research of adaptive and intelligent control studies [1], [2]

  • We prove the optimality of the derived critic control law when using a specific value function

  • Previous work has documented the effectiveness of robust adaptive dynamic programming (RADP) and H∞ in solving nonlinear system robust optimal control problems

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Summary

Introduction

Adaptive dynamic programming (ADP), wherein adaptive parameter identification is combined with conventional dynamic programming to solve nonlinear system optimal control problem forward-in-time, has received increasing attention in the research of adaptive and intelligent control studies [1], [2]. It is difficult to derive an analytical solution to the Hamilton-Jacobi-Bellman (HJB) equation. Neural networks (NNs) and fuzzy systems are generally incorporated as intelligent components for the value approximation [3], [4]. ADP is a promising approach that provides optimal control solutions for complex tasks and has been applied effectively in robotic manipulations [4]–[6], multi-agent systems [7], [8] and power systems [9], [10].

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