Application of Improved PSO and PID Neural Network Controller for Multi-motor Proportion Synchronous Control System

Abstract

PID neural network (PIDNN) controller was designed, and the adaptive mutation particle swarm optimization (PSO) algorithm is adopted to train and update its weights. The adaptive mutation PSO-PIDNN learning algorithm of motor synchronization control was put forward. According to the need of engineering, the algorithm was simplified. In proportion of multiple motor synchronization control system simulation show that the simplified PSO-PIDNN controller also can effectively realize the motor synchronization control, solve the problem of the parameter setting is not easy for traditional PID control, has good stability and fast convergence speed, no overshoot. □□□□□□□□□□□

A Survey of Fuzzy Algorithms Used in Multi-Motor Systems Control

“…This is why the coordinated controllers are based on artificial intelligence techniques, such as neural networks and fuzzy algorithms. An example of an adaptive mutation particle swarm optimization PID neural network learning algorithm of motor synchronization control can be found in [27]. As explained in [3], the most suitable technique for coordinated controller design recognized by researches to date is fuzzy logic control.…”