Adaptive Variants of Optimal Feedback Policies
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We combine adaptive control directly with optimal or near-optimal value functions to enhance stability and closed-loop performance in systems with parametric uncertainties. Leveraging the fundamental result that a value function is also a control Lyapunov function (CLF), combined with the fact that direct adaptive control can be immediately used once a CLF is known, we prove asymptotic closed-loop convergence of adaptive feedback controllers derived from optimization-based policies. Both matched and unmatched parametric variations are addressed, where the latter exploits a new technique based on adaptation rate scaling. The results may have particular resonance in machine learning for dynamical systems, where nominal feedback controllers are typically optimization-based but need to remain effective (beyond mere robustness) in the presence of significant but structured variations in parameters.
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