Abstract
In this paper, an optimal self-learning control scheme for discrete-time nonlinear systems is developed using a new local value iteration based adaptive dynamic programming (ADP) algorithm. The developed local value iteration algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. In the developed local value iteration algorithm, the iterative value function and iterative control law are updated by a subset of the state space. A new analysis method of the convergence property is presented to show that the iterative value functions will converge to the optimum. The convergence criterion for the local value iteration algorithm is presented. A simulation example is given to demonstrate the validity of the present optimal control scheme.
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