Optimization of PID Controller Gain Using Evolutionary Algorithm and Swarm Intelligence

Design of the Proportional-Integral-Derivative (PID) controller for an industrial process represents a challenge due to process complexity and non-linearity. Traditional methods such as Ziegler-Nichols (ZN) for PID controller tuning do not provide an optimal gain; thus, might leave the system with p...

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Bibliographic Details
Main Authors: Maddi, DheerajReddy, Sheta, Alaa, Davineni, Dharani, Al-Hiary, Heba
Format: Conference Proceeding
Language:English
Subjects:
Biological cells
DC motors
GAs
Genetic algorithms
Optimization
Parameter Estimation
PID Controller
PSO
Sociology
Statistics
Tuning
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Summary:Design of the Proportional-Integral-Derivative (PID) controller for an industrial process represents a challenge due to process complexity and non-linearity. Traditional methods such as Ziegler-Nichols (ZN) for PID controller tuning do not provide an optimal gain; thus, might leave the system with potential instability condition and cause significant losses and damages to the system. This paper investigates the merits of evolutionary and swarm-based optimization algorithms in fine-tuning the parameters of a PID controller. Here, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) algorithm were utilized to optimize the PID controller for a DC motor system. Various fitness functions were provided for the presented algorithms to compute the performance of the controller. A new fitness function was proposed to achieve an outstanding control response for the DC motor system. Results demonstrate the efficacy of the proposed methods in improving closed loop system response.
ISSN:2573-3346
DOI:10.1109/IACS.2019.8809144