Applying hybrid genetic–PSO technique for tuning an adaptive PID controller used in a chemical process

The conventional PID controller has static parameters that cannot be changed at different operating conditions. As a result, the term ‘adaptive PID controller’ has appeared to solve this problem. This controller can be tuned using intelligent techniques such as Fuzzy Logic Control, Neural Network Co...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 24; no. 5; pp. 3455 - 3474
Main Authors: El-Gendy, Eman M., Saafan, Mahmoud M., Elksas, Mohamed S., Saraya, Sabry F., Areed, Fayez F. G.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020
Springer Nature B.V
Subjects:
Adaptive control
Adaptive systems
Artificial Intelligence
Artificial neural networks
Butanol
Chemical reactions
Computational Intelligence
Control
Controllers
Distillation
Energy consumption
Engineering
Ethanol
Fuzzy control
Fuzzy logic
Fuzzy systems
Genetic algorithms
Heuristic methods
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Mutation
Network control
Optimization algorithms
Parameters
Particle swarm optimization
Performance evaluation
Proportional integral derivative
Robotics
PID controller
Neural PID tuner
Genetic algorithm
ANFIS PID tuner
Fuzzy gain scheduling PID
Hybrid GA-PSO
Divided wall distillation column
Particle swarm optimization
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:The conventional PID controller has static parameters that cannot be changed at different operating conditions. As a result, the term ‘adaptive PID controller’ has appeared to solve this problem. This controller can be tuned using intelligent techniques such as Fuzzy Logic Control, Neural Network Control, or Adaptive Neuro-Fuzzy Inference Systems. However, the choice of the suitable parameters for these intelligent controllers has a direct effect on their performance. Metaheuristics algorithms—with their powerful performance, speed, and optimal parameter selection—can be applied for choosing controller parameters efficiently. In this paper, a hybrid of genetic algorithm and particle swarm optimization is proposed to tune the parameters of different adaptive PID controllers. To evaluate the performance of the proposed hybrid optimization method on the different adaptive PID controllers, these controllers are applied to control the operation of one of the most difficult chemical processes, the divided wall distillation column. The proposed column used in this work separates a ternary mixture of ethanol, propanol, and n-butanol. Our proposed hybrid optimization technique is compared with the genetic algorithm, and simulation results show that our proposed hybrid genetic-particle swarm technique outperforms genetic algorithm for different disturbances.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-04106-z