Robust Speed Control of Permanent Magnet DC Motors Using an Arctic Puffin Optimized PI Controller and Nonlinear Disturbance Observer

Abstract

Permanent magnet DC (PMDC) motors are widely used in many devices, such as in robotics, medical equipment, and industrial machinery, because they are small and easy to control. However, their operation can be affected by external disturbances such as load fluctuations. Conventional Proportional Integral (PI) controllers, although simple, are not sufficiently robust against such disturbances. This study proposes a novel control scheme for improving PMDC motor performance. It combines a simple PI controller with a Nonlinear Disturbance Observer (NDOB). A key advantage of the NDOB is its enhancement of robustness via actively estimating and compensating lumped disturbances. This makes the system more robust to disturbances and modelling errors while maintaining simplicity of structure and use. The controller parameters (PI gains and the NDOB low pass filter cutoff frequency) have been optimized using a custom algorithm called Arctic Puffin Optimization (APO) that ensure global optimal selection of the tuned parameters. The proposed combined weighted cost function allowed for the best balance between response speed, disturbance rejection, and control effort. The new controller has been tested in MATLAB/Simulink and compared with standard PI controllers. Under step load disturbance, the proposed controller achieves an 88.6% reduction in ITAE compared to conventional PI control. In the presence of sinusoidal load disturbance, the ITAE is further reduced by 94.9%, demonstrating strong disturbance rejection capability. Moreover, under parameter uncertainties, the settling time is improved by 36.8%, while the ITAE is reduced by 56.8%. The results demonstrate improved robustness and faster transient response compared to standard PI control making the proposed controller a superior solution for many applications such as robotic actuators and industrial positioning systems.