Optimized PID and NN-based Speed Control of a Load-coupled DC Motor

In this work, three control strategies are presented, compared, and discussed, applied on a load-coupled DC motor. The purpose is to control in an optimal way the motor speed in terms of the armature voltage. Two strategies are based on PID control, working on the classical PID controller and the optimized one by using particle swarm optimization (PSO) to tune the PID controller parameters. The other strategy is based on neural networks (NNs) where two NNs are built to model and control the system. Based on the results, all the strategies reach excellent performances, however, in terms of system response characteristics like rising time or settling time the PID-based controllers show faster responses than the NN controller. Moreover, by comparing these results with other studies that are working with an unloaded DC motor and even when the working system is more complex, the obtained results have a better performance.