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.