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A policy-iteration-based algorithm is presented in this article for optimal control of unknown continuous-time nonlinear systems subject to bounded inputs by utilizing the adaptive dynamic programming (ADP). Three neural networks (NNs), called critic network, actor network, and quasi-model network, are utilized in the proposed algorithm to give approximations of the control law, the cost function, and the function constituted by partial derivatives of value functions with respect to states and unknown input gain dynamics, respectively.
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