Research Article
PSO-RBFNN Based Optimal PID Controller and ANFIS Based Coupling for Fruits Drying System
@ARTICLE{10.4108/eai.9-3-2021.168961, author={Priya R Krishnan and Remya Gopalakrishnan and R. Nishanth and Abin John Joseph and Agath Martin and Nidhin Sani}, title={PSO-RBFNN Based Optimal PID Controller and ANFIS Based Coupling for Fruits Drying System}, journal={EAI Endorsed Transactions on Energy Web}, volume={8}, number={36}, publisher={EAI}, journal_a={EW}, year={2021}, month={3}, keywords={Fruit drying, Adaptive Neuro Fuzzy Inference System (ANFIS), Propositional-Integral-Derivative (PID) controller, Particle Swarm Optimization (PSO), Fuzzy Logic Controller (FLC)}, doi={10.4108/eai.9-3-2021.168961} }- Priya R Krishnan
Remya Gopalakrishnan
R. Nishanth
Abin John Joseph
Agath Martin
Nidhin Sani
Year: 2021
PSO-RBFNN Based Optimal PID Controller and ANFIS Based Coupling for Fruits Drying System
EW
EAI
DOI: 10.4108/eai.9-3-2021.168961
Abstract
INTRODUCTION: Preservation of fruits by drying is one of the general and important traditional technique followed by the process industries. An accurate controller of relative humidity and temperature is required for the fruit drying control system, which determines the quality of the dried fruits.
OBJECTIVES: To design optimal Propositional-Integral-Derivative (PID) controller based on the Particle Swarm Optimization and Radial Basis Functional Neural Network (PSO-RBFNN) for pineapple drying system.
METHODS: A Propositional-Integral-Derivative (PID) controller based on the Particle Swarm Optimization and Radial Basis Functional Neural Network ( PSO-RBFNN) was proposed in this paper for pineapple drying system. Also, the coupling relationship of relative humidity and temperature is more complicated due to the fluctuations and non-linearity in the drying system. An intelligent Adaptive Neuro Fuzzy Inference System (ANFIS) coupling model is utilized in this paper to access the coupling relationship between relative humidity and temperature.
RESULTS: The proposed control system has been implemented in the MATLAB and results are compared with PID controller, Fuzzy Logic Controller (FLC) and Fuzzy PID controller for the performance constraints such as settling time, peak over shoot and steady state error.
CONCLUSION: The proposed PSO-RBFNN based PID controller gives better control performance with the highly minimized settling time (42 sec for humidity and 40 sec for temperature) and completely eliminated steady state error and Peak overshoot. Finally, the PSO-RBFNN algorithm based PID controller is concluded as the e ffective system.
Copyright © 2021 Priya R Krishnan et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
