Abstract
An online adaptive dynamic programming (ADP) attitude-tracking controller based on policy iteration is proposed, aiming to approach the optimal control of hypersonic vehicles (HVs). The Bellman equation, known as the principal recursive dynamic programming formula, is provided to obtain the controller. In particular, the control action is generated by the ADP controller to track the attitude trajectory. In order to approach optimal control in the uncertain nonlinear HVs system, we use policy iteration to approximate the Bellman equation and build an actor-predictor-critic framework, in which the action network, state estimator and critic network are adopted to implement the policy iteration. Meanwhile, an offline learning method is provided to approach the initial value of iterative computations and improve the efficiency of online learning. The comparative simulations demonstrate the good performance of PIADP with aerodynamic parameter perturbations and random disturbances.
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