Improved-PSO And Its Application In Parameters Tuning Of ADRC

Particle swarm optimization (PSO) algorithm is a kind of random search method based on swarm intelligence, which is developed by simulating the foraging behavior of birds. Because this algorithm is simple, easy to realize and has fast convergence rate, it has been widely used in many disciplines and engineering fields. But it still has some defects, such as low convergence precision, slow convergence speed in the later stage and premature convergence for solving complex problems. So, it has important theoretical significance and application value to do research on improving the algorithm for overcoming these defects.Focus on the poor performance of PSO in solving complex function, an improved-PSO is proposed in this paper. Firstly, the velocity iterative formula is improved on the basis of analyzing the principle of PSO and its improvement principles, to expand the information communication among the elite particles. And then based on the fitness variance and the optimal value to determine whether executive repeat search strategy to improve population diversity, which inhibits particles falling into the local optimum. Finally, implementing wavelet learning strategy strengthens the global searching ability, which can also improve the local refined search and improve the algorithm convergence precision.An immune particle swarm optimization algorithm integrated with Nelder-Mead Simplex Method (NMIPSO) is proposed in this paper, which is on the basis of standard particle swarm algorithm framework. In order to inhibit the particles falling into local optimum and premature convergence, an immune clonal selection operator based on cloud mutation is introduced into the evolution process. NM simplex method is used in the later period to improve the algorithm accuracy. The tests on typical multi-peak functions shows that the improved algorithm can get rid of local optimum and accurately find out the global extreme points of the multi-peak functions.Active Disturbance Rejection Control (ADRC) has too many parameters to adjust, which limits its wide using in engineering. Therefore, NMIPSO is introduced to the parameter optimization for ADRC in this paper. A novel objective evaluating function on the effectiveness of controller is established referring to the performance index of ITAE. The ADRC optimization control system is constructed, in which first-order plus dead-time system is the control object. The simulation results shows that ADRC with parameters adjusted by NMIPSO algorithm shows excellent control performance, strong anti-interference ability and robustness.