Working Performance Improvement of a Novel Independent Metering Valve System by Using a Neural Network-Fractional Order-Proportional-Integral-Derivative Controller

Thanh Ha Nguyen, Tri Cuong Do, Phan Van Du and Kyoung Kwan Ahn ()
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Thanh Ha Nguyen: School of Mechanical and Automotive Engineering, University of Ulsan, 93 Deahak-ro, Nam-gu, Ulsan 44610, Republic of Korea
Tri Cuong Do: School of Mechanical and Automotive Engineering, University of Ulsan, 93 Deahak-ro, Nam-gu, Ulsan 44610, Republic of Korea
Phan Van Du: School of Engineering and Technology, Vinh University, Vinh, Nghe An 43100, Vietnam
Kyoung Kwan Ahn: School of Mechanical and Automotive Engineering, University of Ulsan, 93 Deahak-ro, Nam-gu, Ulsan 44610, Republic of Korea

Mathematics, 2023, vol. 11, issue 23, 1-21

Abstract: In recent years, reducing the energy consumption in a hydraulic excavator has received deep attention in many studies. The implementation of the novel independent metering valve system (NIMV) has emerged as a promising solution in this regard. However, external factors such as noise, throttling loss, and leakage have negative influences on the tracking precision and energy saving in the NIMV system. In this paper, a novel control method, simple but effective, called a neural network-fractional order-proportional-integral-derivative controller is developed for the NIMV system. In detail, the fractional order-proportional-integral-derivative (FOPID) controller is used to improve the precision, stability, and fast response of the control system due to the inclusion of non-integer orders in the proportional, integral, and derivative terms. Along with that, the auto-tuning algorithm of the neural network controller is applied for adjusting five parameters in the FOPID controller under noise, throttling loss, and leakage. In addition, the proposed controller alleviates the amount of calculation for the system by using model-free control. To verify the effectiveness of the proposed controller, the simulation and experiment are conducted on the AMESim/MATLAB and a real test bench. As a result, the proposed controller not only operates the NIMV system accurately in the target trajectory but also reduces energy consumption, saving up 23.33% and 29.25% compared to FOPID and PID in the experimental platform, respectively.

Keywords: energy saving; energy consumption; fractional order-proportional-integral-derivative; independent metering valve; neural network; tracking precision (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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