ew 18: e2

Research Article

PSO-RBFNN Based Optimal PID Controller and ANFIS Based Coupling for Fruits Drying System

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  • @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: Online First},
        volume={},
        number={},
        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
Priya R Krishnan1,*, Remya Gopalakrishnan2, R. Nishanth1, Abin John Joseph1, Agath Martin1, Nidhin Sani1
  • 1: Assistant professor, Cochin University college of engineering kuttanad, Kerala, India
  • 2: Assistant professor, College of engineering Aranmula, Kerala, India
*Contact email: priyashankarkr@gmail.com

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.