International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Simulation-Based Performance Evaluation of PID and Neural Network Controllers for DC Motor Speed Regulation Dr Sandhya Kulkarni 1, Vaibhav Badgujar 2, Kailash Dalui 3, Umesh Bujade 4 1Faculty, Department of Electrical Engineering,Government College of Engineering,Aurangabad,Chh
Sambhajinagar, Maharashtra, India ²UG Student, Department of Electrical Engineering,Government College of Engineering,Aurangabad,Chh Sambhajinagar , Maharashtra, India 3UG Student, Department of Electrical Engineering,Government College of Engineering,Aurangabad,Chh Sambhajinagar , Maharashtra, India 4UG Student, Department of Electrical Engineering,Government College of Engineering,Aurangabad,Chh Sambhajinagar , Maharashtra, India ------------------------------------------------------------------------***------------------------------------------------------------------------Abstract— The paper presents a simulation-based comparative study of speed regulation of a DC motor using Proportional-Integral-Derivative (PID) and Neural Network (NN)-based control. The entire system is modelled and implemented in MATLAB/Simulink. Simulations are performed to evaluate key performance metrics such as rise time, settling time, and steady-state error. The comparative analysis demonstrate that the NN-based controller achieves improved dynamic response and robustness under varying speed compared to the conventional PID controller. The work highlights uses of effectiveness of model controller.
2. METHODOLOGY The methodology involves modeling the DC servo motor, designing a PID controller, and integrating a neural network to enhance control performance. First, the motor's transfer function is derived based on its electrical and mechanical characteristics. The PID controller is then tuned using standard techniques to obtain optimal response parameters [4]. Next, a feed-forward neural network is trained in MATLAB to predict control actions based on error and change in error inputs. The network adjusts the control signal to reduce overshoot and settling time. The hybrid PID-NN control scheme is implemented in Simulink, where performance is evaluated under different input conditions. A comparative analysis is conducted between the standalone PID and the PID-NN controller to highlight the improvement in dynamic response [5].
Keywords—DC servo motor, neural network control, PID controller, speed regulation, performance analysis. 1. INTRODUCTION DC servo motors are widely used in automation and robotics due to their precision and controllability. Speed control of these motors is critical in ensuring efficient and accurate performance. While traditional PID controllers are simple and widely used, they often struggle with nonlinearities and system uncertainties [1].
Mathematical Model The continuous-time transfer function of the DC servo motor is formulated using standard motor parameters. This model is then converted into a discrete-time transfer function using the Z-transform. The resulting discrete model is used as the plant for designing and implementing the digital PID controller in MATLAB/Simulink.
To overcome these limitations, intelligent control methods like Artificial Neural Networks (ANNs) are increasingly used. ANNs can adapt to changing system dynamics and improve control accuracy. This paper presents a hybrid control strategy combining PID and neural network techniques using MATLAB to enhance the speed control of a DC servo motor. Simulation results show improved performance compared to conventional PID control [2], [3].
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Controller Design A discrete-time PID controller is designed and tuned using MATLAB’s PID Tuner tool. The controller parameters are optimized to enhance the system’s stability, minimize overshoot, and reduce steady-state error in both continuous and discrete domains. An Artificial Neural Network (ANN) controller is developed in parallel. The network is trained on the system’s dynamic response data to approximate inverse control behaviour. MATLAB’s Neural Network Toolbox is
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