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• W2896789078 abstract "Summary This paper studies the robust stabilization problem of constrained‐input nonlinear systems with mismatched uncertainties. Initially, the robust stabilization problem is converted into a constrained H 2 optimal control problem by providing a proper infinite‐horizon cost function for the auxiliary system. It is demonstrated that the solution of the constrained H 2 optimal control problem can force the original system to be stable in the sense of uniform ultimate boundedness. Then, under the framework of reinforcement learning, an off‐policy iteration algorithm is proposed to solve the constrained H 2 optimal control problem. The off‐policy iteration algorithm is implemented by using two kinds of approximators. That is, the critic approximator is applied to approximate the optimal cost function and the actor approximator is used to approximate the augmented optimal control. The method of weighted residuals together with the Monte‐Carlo integration technique is employed to determine the weight parameters of critic and actor approximators. Finally, two examples, including a pendulum system, are presented to validate the proposed control algorithm." @default.
• W2896789078 created "2018-10-26" @default.
• W2896789078 creator A5085440585 @default.
• W2896789078 creator A5087825639 @default.
• W2896789078 date "2018-10-15" @default.
• W2896789078 modified "2023-10-10" @default.
• W2896789078 title "An off-policy iteration algorithm for robust stabilization of constrained-input uncertain nonlinear systems" @default.
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• W2896789078 doi "https://doi.org/10.1002/rnc.4342" @default.
• W2896789078 hasPublicationYear "2018" @default.
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