混沌人工鱼群的鲁棒保性能控制权值矩阵优化方法

针对鲁棒保性能控制中的权值矩阵依赖经验选取,无法最大限度的减小系统保守性的问题,提出了一种基于混沌人工鱼群算法的鲁棒保性能控制权值矩阵优化方法.该方法中,将保性能控制鲁棒界作为优化的目标函数来寻找最优权值矩阵是整个算法实现的关键.该种改进的人工鱼群优化算法融合了混沌搜索与自适应步长和视野的人工鱼群优化算法,有效的解决了基本人工鱼群算法的后期收敛速度慢、易陷入局部最优等缺点.通过测试函数对比验证了该种改进人工鱼群优化算法的优越性,并通过应用实例验证了该权值矩阵优化方法的有效性. 

A weighting matrix optimization method for robust guaranteed cost control based on chaos artificial fish swarm algorithm

Herein, a robust guaranteed cost control weighting matrix optimization method based on chaos artificial fish swarm algorithm was proposed to overcome the dependence on the experience of selecting a weighting matrix in order to achieve robust guaranteed cost control and to overcome the inability of the current method to minimize the system conservative. The objective of this methodology is to estimate the optimal weighting matrix by considering the robust guaranteed cost control boundary as an objective function for optimization. The improved artificial fish swarm algorithm combines the chaos search and the artificial fish swarm algorithm with adaptive step and vision, which effectively resolves various drawbacks, including low convergence rate during the latter stage and easiness of being trapped in a local optimal solution, of a basic artificial fish swarm algorithm. The superiority of the improved artificial fish swarm algorithm proposed herein was verified by the contrast results of the test function. Furthermore, the effectiveness of the weighting matrix optimization method was validated using some application examples.