Neural network‐based output synchronization control for multi‐actuator system

Wang, Yunfei; Ding, Haigang; Zhao, Jun; Li, Ran

doi:10.1002/acs.3391

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…Figure 1 shows the multi-cylinder system of hydraulic support in a coal-mining face. Each single hydraulic support moving cylinder system is a typical valve-controlled single-rod cylinder system, where only the position signal can be measured by the dis-placement sensor [36]. However, multiple cylinders of the hydraulic support often move at the same time to meet the requirements of rapid mining, so it is necessary to improve the position accuracy and synchronization accuracy of the multi-cylinder system under disturbance conditions.…”

Section: Problem Formulationmentioning

confidence: 99%

Robust Output Feedback Position Control of Hydraulic Support with Neural Network Compensator

2023

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Hydraulic support is important equipment in the fully mechanized mining face, and the control performance of the hydraulic support multi-cylinder system directly affects the smooth progress of coal mining process, which is the basis for the continuous advancement of the coal face. However, the friction force of the hydraulic support in the process of pulling the frame is complex due to the underground environmental load. Moreover, the parameters of the moving cylinder are uncertain, and the state of the system cannot be fully measured, which increases the difficulty of control. A proportional-integral-derivative controller is usually used in electro-hydraulic closed-loop control systems because of its computational complexity, but its robustness is poorly adapted to variable load conditions in the coal mine. Therefore, a robust output feedback position controller is proposed in this paper to improve control accuracy and system robustness with only position signal. The multi-cylinder system of hydraulic support is modeled as a standard type, and then a high-order differentiator is proposed to estimate the immeasurable system states using the output position signal. A neural network compensator is applied to estimate and compensate for the external disturbance of the moving cylinder. Furthermore, the parameters of the ZY3200/08/18D hydraulic support are adopted to analyze the effectiveness of the designed controller in simulations. Finally, a real-time control system of hydraulic support is built, and the experimental results show that the novel robust output feedback controller has improved by 47.2% and 30.6% in tracking accuracy compared to PI controller.

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Section: Problem Formulationmentioning

confidence: 99%

Robust Output Feedback Position Control of Hydraulic Support with Neural Network Compensator

2023

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“…With the development of automation and intelligence in coal mines, electro-hydraulic control technology has attracted more and more attention [1]. Compared with mechanical transmission and electrical transmission, hydraulic transmission has a higher power-toweight ratio, which has been widely used in the mining field, such as in coal shearers and hydraulic supports [2,3]. However, the electro-hydraulic system is hard to control because of the strong nonlinearities, parameter uncertainties, modelling errors and external disturbances that are present [4].…”

Section: Introductionmentioning

confidence: 99%

An Output Cooperative Controller for a Hydraulic Support Multi-Cylinder System Based on Neural Network Compensation

Wang,

Zhao,

Zhang

et al. 2024

Mathematics

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The straightness control of a fully mechanized working face is the key technology used in intelligent coal mining, so the position control of a hydraulic support multi-cylinder moving system is of great significance. However, due to the harsh environment of coal mines, complex friction, external disturbances, and the coupling relationship between adjacent cylinders, the accuracy of position control is restricted. Therefore, an output cooperative controller is proposed in this paper for a multi-cylinder system. A high-order sliding mode observer is utilized to estimate the system states with the only available output position signal. A neural network disturbance observer is applied to estimate the lump disturbance of the strict-feedback model, including the system uncertainty, disturbance force and the coupling force between adjacent cylinders. Then, continuous motion position tracking simulation is conducted and the estimation performance of the state observer and neural network is analyzed. Furthermore, a multi-cylinder collaborative control test rig is designed, and experiments based on the actual actions of the hydraulic support are conducted. The results show that the proposed output cooperative controller has an excellent position control performance compared with the traditional proportional–integral controller.

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“…Therefore, high performance motion control of hydraulic systems is necessary to achieve fast and high precision automatic drilling of blast holes by rock drilling jumbo. However, hydraulic systems have strong parameters uncertainties, for instance, the elastic modulus of hydraulic oil, flow/signal gain, load mass, and viscous damping coefficient, as well as the unmodeled dynamics of the system, such as unmodeled friction, external disturbances, and so forth 5–10 . These factors bring some challenges to the model‐based control algorithm design.…”

Section: Introductionmentioning

confidence: 99%

“…However, hydraulic systems have strong parameters uncertainties, for instance, the elastic modulus of hydraulic oil, flow/signal gain, load mass, and viscous damping coefficient, as well as the unmodeled dynamics of the system, such as unmodeled friction, external disturbances, and so forth. [5][6][7][8][9][10] These factors bring some challenges to the model-based control algorithm design.…”

mentioning

confidence: 99%

Parameter adaptive based neural network sliding mode control for electro‐hydraulic system with application to rock drilling jumbo

Guo,

Wang,

Liu

2024

Adaptive Control & Signal

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SummaryRock drilling jumbo is an important large construction machine used for tunneling construction, and its automation has an urgent demand in engineering. However, the electro‐hydraulic system of the rock drilling jumbo has strong parameters uncertainties and some dynamics that are hard to model accurately, which causes certain challenges for designing model‐based high‐performance control algorithms. To solve these challenges, a parameter adaptive based neural network sliding mode control algorithm is proposed to enhance control performance of the electro‐hydraulic system. The parameter adaptive law is developed to estimate unknown parameters of the system, the neural network is applied for compensating unmodeled dynamics, and then the final control law is designed by sliding mode control method, and the stability demonstration of the closed‐loop system is given. In the simulations, the effectiveness of the designed parameter adaptive law is verified. Extensive comparison experiments are performed on a real rock drilling jumbo driven by proportional valves, the experimental results demonstrate that the developed control algorithm obviously improves the control precision of hydraulic cylinder of the rock drilling jumbo compared with the traditional sliding mode and PID control algorithm, thus the designed control algorithm can be expanded and applied for general hydraulic servo control mechanism.

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Neural network‐based output synchronization control for multi‐actuator system

Cited by 10 publications

References 37 publications

Robust Output Feedback Position Control of Hydraulic Support with Neural Network Compensator

Robust Output Feedback Position Control of Hydraulic Support with Neural Network Compensator

An Output Cooperative Controller for a Hydraulic Support Multi-Cylinder System Based on Neural Network Compensation

Parameter adaptive based neural network sliding mode control for electro‐hydraulic system with application to rock drilling jumbo

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