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

  • Authors:

  • R. C. Hu;Z. G. Ying;W. Q. Zhu

  • Affiliations:

  • Department of Mechanics, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, People's Republic of China 310027;Department of Mechanics, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, People's Republic of China 310027;Department of Mechanics, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, People's Republic of China 310027

  • Venue:
  • Structural and Multidisciplinary Optimization
  • Year:
  • 2014

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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.