Ruppell's Fox Optimizer for controlling DC motor
Keywords:
Rüppell's Fox Optimize, PID Tuning, DC Motor Control, Artificial Intelligence, MetaheuristicsAbstract
This paper presents the implementation and evaluation of the Rüppell's Fox Optimizer (RFO) algorithm for tuning Proportional-Integral-Derivative (PID) controllers in DC motor applications. The RFO algorithm was developed and tested using MATLAB/Simulink on a standard laptop configuration. Initially, RFO's optimization performance was benchmarked against Particle Swarm Optimization (PSO) using CEC2017 benchmark functions across unimodal, multimodal, and fixed multimodal test categories. Results demonstrated RFO's superior convergence accuracy and global optimization capabilities, achieving better best, mean, and worst scores with lower standard deviations and first-rank positioning in all benchmark categories. The RFO algorithm exhibited faster initial convergence but sometimes plateaued compared to PSO's persistent long-term improvement. Subsequently, the RFO optimization technique was applied to tune PID controller parameters for DC motor speed control. Performance comparison with conventional PID and PSO-PID controllers showed that the proposed RFO-PID method delivered exceptional control performance with zero overshoot, fastest settling time (1.72 seconds), and lowest ITSE value (0.0813) over a 0-4 second evaluation period. The RFO-PID controller closely tracked the reference signal, outperforming both conventional PID and PSO-PID methods in terms of stability, accuracy, and response speed, demonstrating its effectiveness for precise DC motor control applications.
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