Abstract
Classical gradient-based approximate dynamic programming approaches provide reliable and fast solution platforms for various optimal control problems. However, their dependence on accurate modeling approaches poses a major concern, where the efficiency of the proposed solutions are severely degraded in the case of uncertain dynamical environments. Herein, a novel online adaptive learning framework is introduced to solve action-dependent dual heuristic dynamic programming problems. The approach does not depend on the dynamical models of the considered systems. Instead, it employs optimization principles to produce model-free control strategies. A policy iteration process is employed to solve the underlying Hamilton–Jacobi–Bellman equation using means of adaptive critics, where a layer of separate actor-critic neural networks is employed along with gradient descent adaptation rules. A Riccati development is introduced and shown to be equivalent to solving the underlying Hamilton–Jacobi–Bellman equation. The proposed approach is applied on the challenging weight shift control problem of a flexible wing aircraft. The continuous nonlinear deformation in the aircraft’s flexible wing leads to various aerodynamic variations at different trim speeds, which makes its auto-pilot control a complicated task. Series of numerical simulations were carried out to demonstrate the effectiveness of the suggested strategy.
Funder
Ontario Centres of Excellence
Subject
Artificial Intelligence,Control and Optimization,Mechanical Engineering
Cited by
12 articles.
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