Efficient Control Of A Nonlinear Double-Pendulum Overhead Crane With Sensorless Payload Motion Using An Improved PSO-Tuned PID Controller

Mohamed, Zaharuddin and Jaafar, Hazriq Izzuan and Mohd Subha, Nurul Adilla and Husain, Abdul Rashid and Ismail, Fatimah Sham and Ramli, Liyana and Tokhi, Mohammad Osman and Shamsudin, Mohamad Amir (2019) Efficient Control Of A Nonlinear Double-Pendulum Overhead Crane With Sensorless Payload Motion Using An Improved PSO-Tuned PID Controller. Journal Of Vibration And Control, 25 (4). pp. 907-921. ISSN 1077-5463

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Official URL: https://journals.sagepub.com/doi/abs/10.1177/10775...

Abstract

This paper proposes an efficient proportional–integral–derivative (PID) control of a highly nonlinear double-pendulum overhead crane without the need for a payload motion feedback signal. Optimal parameters of the PID controllers are tuned by using an improved particle swarm optimization (PSO) algorithm based on vertical distance oscillations and potential energy of the crane. In contrast to a commonly used PSO algorithm based on a horizontal distance, the approach resulted in an efficient performance with a less complex controller. To test the effectiveness of the approach, extensive simulations are carried out under various crane operating conditions involving different payload masses and cable lengths. Simulation results show that the proposed controller is superior with a better trolley position response, and lower hook and payload oscillations as compared to the previously developed PSO-tuned PID controller. In addition, the controller provides a satisfactory performance without the need for a payload motion feedback signal.

Item Type:	Article
Uncontrolled Keywords:	Double-pendulum crane, fitness function, proportional-integral-derivative, particle swarm optimization, oscillation control, PID Controller
Subjects:	T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery
Divisions:	Faculty of Electrical Engineering > Department of Control, Instrumentation & Automation
Depositing User:	Mohd Hannif Jamaludin
Date Deposited:	28 Jul 2020 12:44
Last Modified:	28 Jul 2020 12:44
URI:	http://eprints.utem.edu.my/id/eprint/24089
Statistic Details:	View Download Statistic

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