International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-13 Issue-134 January-2026
  1. 4774
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  2. 2.8
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Design and intelligent tuning of a proportional–integral–derivative controller for an armature-controlled DC motor

Anbarasu P1, Loganathan N2, Narendran A3 and Rameshkumar K4

Assistant Professor, Department of Electrical and Electronics Engineering,Sri Eshwar College of Engineering, Coimbatore,Tamil Nadu,India1
Assistant Professor, Department of Electrical and Electronics Engineering,Sri Krishna College of Engineering and Technology, Coimbatore,Tamil Nadu,India2
Assistant Professor, Department of Electrical and Electronics Engineering,SRM Madurai College for Engineering and Technology, Madurai,Tamil Nadu,India3
Associate Professor, Department of Electrical and Electronics Engineering,Dr. Mahalingam College of Engineering and Technology, Pollachi,Tamil Nadu,India4
Corresponding Author : Anbarasu P

Recieved : 02-January-2025; Revised : 07-January-2026; Accepted : 15-January-2026

Abstract

Stability, resilience, and optimal performance are critical goals in the field of process control, particularly in the design of proportional-integral-derivative (PID) controllers for direct current (DC) motors. To identify the most suitable PID controller parameters for various industrial applications, it is essential to evaluate the effectiveness of different tuning approaches. This study considers three PID controller design methods: the traditional Ziegler–Nichols (ZN) technique, the particle swarm optimization (PSO) algorithm, and a novel strategy based on the random forest (RF) algorithm. The ZN method provides a heuristic-based tuning approach derived from the dynamic response of the DC motor. The PSO algorithm employs a metaheuristic optimization process that iteratively adjusts PID parameters to minimize a predefined objective function. In addition, this study explores the use of the RF technique to automatically estimate and optimize PID controller parameters using historical DC motor performance data. Simulation results obtained using MATLAB demonstrate that the RF-based tuning approach outperforms both the ZN- and PSO-based controllers. Specifically, the RF-tuned PID controller achieves the best rise time (Tr) of 0.0084 s and settling time (Ts) of 0.0227 s, with no maximum peak overshoot (Mp) and negligible error indices, including an integral of squared error (ISE) of 0.00218, an integral of absolute error (IAE) of 0.1102, and an integral of time-weighted absolute error (ITAE) of 0.0489. Furthermore, convergence analysis confirms the ability of the RF approach to consistently attain lower cost function values (J = 0.010) across all trials, achieving a 100% success rate and demonstrating strong robustness and repeatability.

Keywords

PID controller, DC motor control, Random forest, Particle swarm optimization, Ziegler–nichols tuning, MATLAB simulation.

Cite this article

P A, N L, A N, K R. Design and intelligent tuning of a proportional–integral–derivative controller for an armature-controlled DC motor. International Journal of Advanced Technology and Engineering Exploration. 2026;13(134):85-101. DOI : 10.19101/IJATEE.2025.121220008

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