An optimized proportional-integral-derivative controller by particle swarm optimization and back propagation neural network for a direct current motor servo control system

The proportional-integral-derivative (PID) controller is the most common controller in industrial production. However, the PID controller cannot change own parameters once it is designed, so that the conventional PID control method is difficult to cope with complex nonlinear systems. In order to overcome the shortcomings of classical PID controller, an optimized control method based on the back propagation (BP) neural network has been proposed. At the same time, the particle swarm optimization (PSO) is combined with the BP neural network to avoid BP neural network falling into local optimization. The validity of combined PSO-BP PID optimization algorithm is verified by a direct current motor servo control system. Results show that comparing with the PID, the overshoot of PSO-BP-PID to the response of step signal is reduced by 88.84%, the setting time and rise time is reduced by 61.88% and 34.47% respectively. The PSO-BP PID controller for direct current motor servo control system is more reasonable and effective than PID controller.