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• W4231524150 abstract "In this chapter, a finite horizon iterative adaptive dynamic programmingAdaptive dynamic programming (ADP) algorithm is developed to solve the optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state. A new $$epsilon $$ -optimal control algorithm based on the iterative ADP approach is developed that makes the iterative value function converge to the greatest lower bound of all performance indices within an error $$epsilon $$ in finite time. The convergence analysis of the present ADP algorithm in terms of the iterative value function and control law is conducted. The optimal number of control steps can also be obtained by the present $$epsilon $$ -optimal control algorithm for the unfixed initial state. Neural networks are used to approximate the performance index function, and compute the optimal control law, respectively, for facilitating the implementation of the $$epsilon $$ -optimal control algorithm. Finally, a simulation example is given to show the effectiveness of the present method." @default.
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• W4231524150 date "2017-06-14" @default.
• W4231524150 modified "2023-10-02" @default.
• W4231524150 title "An Iterative $$epsilon $$ -Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems with Unfixed Initial State" @default.
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• W4231524150 doi "https://doi.org/10.1007/978-981-10-4080-1_2" @default.
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