面向航天器顺光成像均衡博弈混合优化方法OA

Under the condition that both parties'maneuvering capabilities and strategy spaces are known,efficiently obtaining the Nash equilibrium solution of spacecraft orbital games is crucial for gaining game advantages.To solve the Nash equilibrium problem in the sunlit positioning and imaging game of spacecraft,this study proposes a hybrid optimization algorithm based on the harmony search-sequential quadratic programming(HS-SQP).First,a sunlit positioning and imaging game model that considers multiple constraints such as impulsive maneuvers,illumination constraints,relative distance,and fuel consumption is established,transforming the continuous-time game problem into a discrete nonlinear programming problem.On this basis,an algorithm framework combining the HS global search with SQP local optimization is designed,and a Pareto-Nash equilibrium screening mechanism is introduced to ensure that the Pareto-optimal solutions are selected and both players in the game converge to the Nash equilibrium,so as to guarantee that the decisions of both sides are optimal.Simulation results show that the HS-SQP algorithm not only successfully solves the Nash equilibrium in the sunlit imaging game,but also achieves a solution success rate of more than 97%in Monte Carlo random simulations,demonstrating good robustness and adaptability.Comparative simulations illustrate that the solution time of the proposed algorithm is reduced by 33.6%and 64.3%compared with the Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),respectively,and the solution accuracy is improved by 11.41%and 12.17%compared with PSO and the standard HS,respectively.The results verify that the algorithm improves computational efficiency and overall performance while ensuring high-precision solutions.It is suitable for solving complex stochastic orbital game strategy problems,and can provide support for online decision-making and real-time game confrontation in spacecraft sunlit imaging missions.