Design and simulation of a PID neural network controller for PMDC motor speed control

Direct current (DC) motors have many difficulties when controlling angular velocity in a variety of applications. The perfect controller cannot be carried out by traditional control alone due to the nonlinear properties of DC motors, design constraints, and mechanical variation caused by the operation conditions. This study proposes a design for an artificial neural network-based PID controller (ANN-PID) to control the speed of a permanent magnet DC motor (PMDC) in two methods. A detailed analysis is performed based on the simulation results of both methods. The proposed controllers are numerically simulated for various test conditions including; set-point changes, step changes in the load torque, and parameter variations, then the suggested techniques were compared in a comparative study with a traditional PID controller based on the transient response specifications and the performance indices to validate the performance of the controllers. The simulation results demonstrated that the controllers have improved dynamics, static performance, and less overshoot. The methods described here achieve control more effectively than the conventional control approaches under both nominal and disturbed test conditions over different operating ranges.