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In this paper a game-theoretic adaptive learning algorithm based on an action-dependent value function (Q-function) is proposed to solve the optimal tracking control problem with adversarial inputs and completely unknown system and reference dynamics. In order to convert the tracking problem to a regulation problem we augment the system and the reference states and pick appropriately the user-defined...
In this technical note, an online learning algorithm is developed to solve the linear quadratic tracking (LQT) problem for partially-unknown continuous-time systems. It is shown that the value function is quadratic in terms of the state of the system and the command generator. Based on this quadratic form, an LQT Bellman equation and an LQT algebraic Riccati equation (ARE) are derived to solve the...
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