View Video Presentation: https://doi.org/10.2514/6.2022-0613.vid A model-based approximate dynamic programming (ADP) controller is applied to a hypersonic vehicle (HSV) with time-varying aerothermoelasatic effects for approximate optimal state regulation. To account for aerothermoelastic parameter variations, the nominal HSV dynamic model is discretely switched over time to better reflect changes caused by the parameters. A Lypunov-based analysis is leveraged to design the actor-critic update laws for the reinforcement learning ADP approach and to prove uniformly ultimately bounded convergence of the switched dynamic system. Simulation results are included, which indicate that the switched model-based ADP controller yields HSV state regulation in the presence of disturbances.
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Greene et al. (2022) studied this question.
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