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A policy iteration method is proposed to solve the optimal tracking control of continuous-time systems based on HJB equation. The performance index function is composed by the state tracking error and the tracking control error. The iterative performance index function and the iterative control are obtained by the presented policy iteration. It is proven that the iterative control makes the system...
This paper presents a novel model-free online algorithm to solve the linear continuous-time three-player zero-sum differential game problem in the presence of dynamic uncertainty. Based on robust adaptive dynamic programming (RADP) method, the saddle point control policy and disturbance policy are iteratively approximated for the game problem with unmatched uncertainties. The convergence of the proposed...
This paper presents a novel adaptive/approximate dynamic programming algorithm to solve the H∞ control problem of constrained-input continuous-time nonlinear systems. The developed algorithm employs a single critic neural network (NN) to derive the approximate solution of the Hamilton-Jacobi-Isaacs equation. With two additional terms introduced, namely, the stabilizing term and the robustifying term...
In this paper, an optimal self-learning cooperative control for heterogeneous multi-agent systems by iterative adaptive dynamic programming (ADP) is developed. The main idea is to design an optimal control law by policy iteration based ADP technique which makes all the agents track a given dynamics and simultaneously makes the iterative performance index function reach the Nash equilibrium. The cooperative...
In this paper, an optimal control design scheme is proposed for continuous-time linear stochastic systems with unknown dynamics. Both signal-dependent noise and additive noise are considered. A non-model based optimal control design methodology is employed to iteratively update the control policy online by using the system state and input information. A new adaptive dynamic programming algorithm is...
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