Disturbance Observer-Based Bio-Inspired LQR Optimization for DC Motor Speed Control

This research presents an optimized DC motor controller designed to enhance performance and efficiency in speed regulation, acknowledging the pivotal role of control strategies in modern engineering applications. The controller maximizes the capabilities of the integral linear quadratic regulator (ILQR) framework, fine-tuned using state-of-the-art particle swarm optimization (PSO) techniques and a well-defined cost function alongside other bio-inspired algorithms. Additionally, a disturbance observer is incorporated into the LQR scheme to improve the system's resistance to external disturbances, both constant and time-varying. PSO and genetic algorithms (GA) are employed to identify appropriate LQR weighting, significantly increasing control performance. This integration produces a robust control system to improve the performance and efficiency of DC motor speed regulation. It provides an elaborate structure that can be adapted to various technical applications. Numerous simulations demonstrate the enhanced performance of the developed technique in achieving optimal speed control while maintaining high robustness.