Parameter Optimization of Cascade Dual PI Control for Series-Wound DC Motor Using GA, PSO, and GWO Algorithms
Keywords:
Series-Wound DC Motor, Dual PI Controller, GA, PSO, GWO, Speed Control, MATLAB/SimulinkAbstract
In this study, parameter optimization of a cascade dual PI controller structure was performed for
the speed and torque control of a series-wound direct current (DC) motor. Since the field winding and
armature circuit are connected in series in series-wound DC motors, the magnetic flux is directly dependent
on the armature current, causing the system to exhibit highly nonlinear dynamics. In the proposed cascade
control architecture, the outer loop tracks the motor speed to generate the reference armature current, while
the inner loop controls the armature current to generate the armature voltage command. The mathematical
model of the DC motor was constructed in the MATLAB/Simulink environment based on armature voltage,
back electromotive force (EMF), current-dependent variable magnetic flux, electromagnetic torque, and
mechanical speed dynamics. The four fundamental gain parameters of the dual PI controller (Kp(i), Ki(i),
Kp(w), Ki(w)) were optimized using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and
Grey Wolf Optimizer (GWO) methods. Simulation results were compared among the uncontrolled system,
GA-PI, PSO-PI, and GWO-PI structures. The findings demonstrate that the dual PI structure tuned with
metaheuristic optimization algorithms successfully limits high current and torque fluctuations during
starting while ensuring stable speed tracking.
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