What question did this study set out to answer?

The aim is to optimize a two-degree-of-freedom PID controller for effective speed regulation of DC motors using animated oat optimization.

April 21, 2026Open Access

Enhanced DC motor speed regulation using two-degree-of-freedom PID controller tuned by animated oat optimization with simulation and real-time experimental validation

Key Points

The aim is to optimize a two-degree-of-freedom PID controller for effective speed regulation of DC motors using animated oat optimization.
Utilized animated oat optimization for tuning the parameters of the 2-DOF PID controller.
Conducted comparative simulations against various metaheuristic algorithms under controlled settings.
Performed experimental validation in a real-time hardware setup to confirm simulation results.
AOO algorithm achieved the best average cost value of 65.2185 with high convergence stability.
The AOO-based controller demonstrated the shortest rise time of 0.9217 seconds and a settling time of 1.4143 seconds.
Experimental validation showed accurate speed tracking across different reference inputs, confirming simulation findings.

Abstract

This paper proposes a novel intelligent control framework for precise speed regulation of DC motors using a two-degree-of-freedom proportional–integral–derivative (2-DOF PID) controller whose six parameters are optimally tuned via the animated oat optimization (AOO) algorithm. AOO is a nature-inspired metaheuristic derived from the hygroscopic seed dispersal behavior of Avena sterilis L., offering a hybrid global-local search strategy that balances exploration and exploitation through mechanisms such as eccentric rotation, projectile jumps, and Lévy flights. The controller parameters are optimized within bounded physical limits using a composite cost function that integrates Integral Absolute Error (IAE) and normalized overshoot percentage. Comparative simulations are conducted against four recent metaheuristics, catch fish optimization algorithm (CFOA), greater cane rat algorithm (GCRA), RIME optimization, and particle swarm optimization (PSO), under identical settings (100 iterations, 30 agents). Statistical analysis over 25 independent runs reveals that the AOO algorithm achieves the best average cost value (65.2185) with the lowest standard deviation (1.2091), indicating high convergence stability. In time-domain analysis, the AOO-based 2-DOF PID controller exhibits the shortest rise time (0.9217 s), fastest settling time (1.4143 s), lowest overshoot (0.5688%), and negligible steady-state error (∼10 −13 ), outperforming all benchmark algorithms. The controller’s robustness and adaptability are further validated through simulations under random reference inputs, multistep transitions, and external disturbances with measurement noise. Experimental validation using a real-time hardware-in-the-loop setup, including a Pololu 12 V DC geared motor, IBT-2 H-bridge driver, Arduino Uno, and MATLAB/Simulink interface, confirms the simulation results, demonstrating fast, stable, and accurate speed tracking across diverse reference profiles. These findings confirm that the AOO-based 2-DOF PID controller offers a highly effective, reliable, and practically deployable solution for high-performance motor control under dynamic and uncertain operating conditions.

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Cite This Study

Yüksek et al. (Sun,) studied this question.

synapsesocial.com/papers/69e713fdcb99343efc98d739 https://doi.org/https://doi.org/10.1177/00202940261442256

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