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针对含有不稳定模态的高超声速飞行器平衡状态的求解问题,提出了遗传算法-序列二次规划(GA-SQP)混合优化求解算法。该算法基于GA,根据时间乘以误差绝对值积分(ITAE)性能指标,采用混沌搜索和淘汰机制,将配平问题转化为代价函数最小值的求解问题。此外,在局部搜索中引入SQP策略,分步求解升降舵偏角和油门设置,以及迎角初始值。通过建立基于Simulink的动态模型进行仿真,结果表明,该算法能够精确地收敛到平衡点,并具有较好的稳定性,而且与初始值无关。该算法为一类复杂非线性系统平衡状态的求解问题提供了一种实用有效的解决方法。
In order to solve the problem of the equilibrium state of hypersonic vehicles with unstable modes, a Genetic Algorithm - Sequential Quadratic Programming (GA-SQP) hybrid optimization algorithm is proposed. The algorithm is based on GA, and according to the time multiplied by ITAE performance index, chaos search and elimination mechanism is adopted to convert the trim problem into the solution of the minimum cost function. In addition, the SQP strategy is introduced in the partial search to solve the elevator declination and throttle setting step by step, as well as the initial angle of attack. The simulation results show that the proposed algorithm can accurately converge to the equilibrium point with good stability and has nothing to do with the initial value. The algorithm provides a practical and effective solution to the problem of solving the equilibrium state of a class of complex nonlinear systems.