Aeroengine controller design based on improved particle swarm optimization
Jiyang Dai, Fangting Huang, Ying Jin, Huazhu Luo, Ying Li · 2016
Considering the shortage of intelligent algorithm in the multi-objective optimization, in this paper, we proposed a particle swarm optimization(PSO) algorithm which adaptive adjustment learning factor. The algorithm dynamically adjust the learning factor to make it avoid falling into local minima, then with the help of average thought, choosing the best through the comparison of fitness and average value. While introducing shrinkage factor, improve the convergence of algorithm. Then compared with the standard pso algorithm and other improved pso algorithm in the classic functions, the effect is superior. Finally, simulating on the aeroengine multivariable robust H∞controller design, the simulation results show that the algorithm has good performance.