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Stochastic minimax optimal control strategy for uncertain quasi-Hamiltonian systems using stochastic maximum principle

Research paper by R. C. Hu, Z. G. Ying, W. Q. Zhu

Indexed on: 05 Jul '13Published on: 05 Jul '13Published in: Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization



Abstract

A stochastic minimax optimal control strategy for uncertain quasi-Hamiltonian systems is proposed based on the stochastic averaging method, stochastic maximum principle and stochastic differential game theory. First, the partially completed averaged Itô stochastic differential equations are derived from a given system by using the stochastic averaging method for quasi-Hamiltonian systems with uncertain parameters. Then, the stochastic Hamiltonian system for minimax optimal control with a given performance index is established based on the stochastic maximum principle. The worst disturbances are determined by minimizing the Hamiltonian function, and the worst-case optimal controls are obtained by maximizing the minimal Hamiltonian function. The differential equation for adjoint process as a function of system energy is derived from the adjoint equation by using the Itô differential rule. Finally, two examples of controlled uncertain quasi-Hamiltonian systems are worked out to illustrate the application and effectiveness of the proposed control strategy.