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Adaptive dynamic programming is a collective term for a variety of approachesto infinite-horizon optimal control. Common to all approaches is approximationof the infinite-horizon cost function based on dynamic programming philosophy.Typically, they also require knowledge of a dynamical model of the system. Inthe current work, application of adaptive dynamic programming to a system whosedynamical model is unknown to the controller is addressed. In order to realizethe control algorithm, a model of the system dynamics is estimated with aKalman filter. A stacked control scheme to boost the controller performance issuggested. The functioning of the new approach was verified in simulation andcompared to the baseline represented by gradient descent on the running cost.

Stacked adaptive dynamic programming with unknown system model