| In recent years, with the continuous improvement of production level inmanufacturing and processing industrial field, control system performance for big scaleproduction equipments is required higher and higher. Synchronous control performanceimpacts not only the product quality, but also the sales. So it has great important practicalsignificance how to improve multi-motor synchronous control system performance in orderto increase its precision.At present, there are mainly two problems for multi-motor synchronous control, firstis following of given speed for single motor. For realizing synchronous running formultiple motors, single motor must have better control effect, which demands thatcontroller of every motor has good dynamic response ability. Conventional PID controllerapplies widely in industrial production, it processes the characteristic of simple structureand easy to realize. But, once ensured PID parameters in the control process can’t bechanged, when confronted with such nonlinear, time-variant and strong coupling controltarget in multi-motor synchronous control system, the control result is not ideal. So thispaper designs the neuron PID controller processing self-learning ability based onconventional PID controller, in the dynamic load disturbance cases, which can set PIDparameters online in real time, it is improved in the course of deep research, design aneuron PID controller based on Proportional Summation Derivative (PSD) algorithmic.Simulation test in Matlab7.0environment proved the effectiveness of proposed method.Another problem is coordinating synchronous control for multiple motors. Comparedwith parallel control and master-slave control, deviation coupling control acquires bettercontrol effect to solve multi-motor synchronous control problem. But the speedcompensator of traditional deviation coupling control couldn’t change coupling orderbetween motors on the basis of motor running state, which weakened synchronous controlperformance between motors. This paper designs the speed compensator based on fuzzycontrol, and improves traditional deviation coupling control. When motor running state changed, improved deviation coupling control could adapt coupling of multiple motors inreal time. Simulation test showed the parallel control performance improved obviously. |
