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In this paper, we focus on developing adaptive optimal regulators for a class of continuous-time nonlinear dynamical systems through an improved neural learning mechanism. The main objective lies in that establishing an additional stabilizing term to reinforce the traditional training process of the critic neural network, so that to reduce the requirement with respect to the initial stabilizing control,...
In this paper, we investigate the neural-network-based adaptive guaranteed cost control for continuous-time affine nonlinear systems with dynamical uncertainties. Through theoretical analysis, the guaranteed cost control problem is transformed into designing an optimal controller of the associated nominal system with a newly defined cost function. The approach of adaptive dynamic programming (ADP)...
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