Abstract
In this paper, a control method for a hydraulic loading system of an electromechanical platform based on a fractional-order PID (Proportion-Integration-Differentiation) controller is proposed, which is used to drive the loading system of a mechatronic journal test rig. The mathematical model of the control system is established according to the principle of the electro-hydraulic system. Considering the indetermination of model parameters, the method of parameter identification was used to verify the rationality of the theoretical model. In order to improve the control precision of the hydraulic loading system, the traditional PID controller and fractional-order PID controller are designed by selecting appropriate tuning parameters. Their control performances are analyzed in frequency domain and time domain, respectively. The results show that the fractional-order PID controller has better control effect. By observing the actual control effect of the fractional-order PID controller on the journal test rig, the effectiveness of this control algorithm is verified.
Highlights
A journal bearing test rig as a typical electromechanical platform was developed by authors to study the integrated performance of journal bearing under various load rolling
The results have present, research on the fractional order PID (FOPID) controller in the loading system of a bearing test rig is limited
The transfer function model was obtained by system parameter identification based on the ARMAX model
Summary
A journal bearing test rig as a typical electromechanical platform was developed by authors to study the integrated performance of journal bearing under various load rolling. Karam and Elbayomy et al proposed the PID controller based on a genetic algorithm to control the electro-hydraulic servo system, and proved its effectiveness through experiments [1]. Truong and Ahn use the proportional integral derivative (PID) control method, as well as an online self-tuning fuzzy-neural mechanism to improve the control performance of the loading system [11]. Yao et al developed and applied a neural network adaptive inverse controller to an electro-hydraulic servo system which is capable of tracking desired signals with high accuracy, and has good real-time performance [12]. The results have present, research on the FOPID controller in the loading system of a bearing test rig is limited.
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