Order-reduced-model based integral sliding mode control of a heavy-duty hydraulic manipulator with disturbances estimation

Xinping, Guo; Wang, Hengsheng; He, Xin; Liu, Hui

doi:10.1177/09596518211058548

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…Formula ( 5) is the calculation of PMSM1 compensation error with the introduction of speed comprehensive evaluation index ω p [18].…”

Section: Plos Onementioning

confidence: 99%

Research on coupling control of multiple permanent magnet synchronous motors based on NAISMC and SMDO

Hao,

Zhao

2023

PLoS ONE

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The synchronous control system of multi-permanent magnet motor has the characteristics of many parameter variables and mutual coupling. The use of sliding mode control to optimize the parameters in the multi-permanent magnet motor system not only ensures the stability of the system operation, but also improves the control accuracy of the system, which is of great importance in practical applications. Based on this background, the study combines the new adaptive integral sliding mode control (NAISMC) with the improved sliding-mode disturbance observer (SMDO) and uses it for the multi-permanent magnet synchronous motor (MPMSM). In NAISMC, the controller updates and adjusts the parameters of the controller using an adaptive algorithm according to the state of the system and the error signals, which further improves the stability and robustness of the system. SMDO utilizes the principle of the sliding-mode observer to estimate the disturbance of the system, and eliminates the effect of the disturbance on the system by introducing a compensation term. The sliding mode observer calculates the disturbance estimate by comparing the difference between the actual and the estimated outputs. The disturbance estimate is finally used to generate the corresponding compensation signal to eliminate or minimize the effect of the disturbance on the system. NAISMC is combined with SMDO and used in the deviation coupling control of MPMSM. The study established a simulation experiment environment in MATLAB, set the simulation time to 0.4s, and the rated speed of the motor to 1000r/min. The improved sliding mode control scheme is tested, and the results show that the motor output speed, tracking error and electromagnetic torque variation under the improved sliding mode control scheme are smaller than those under the traditional sliding mode control scheme. Under the same simulation conditions, the multi-motor speed synchronization error under the improved sliding mode control scheme is around 0r/min, and its error value is close to 0, so the control effect is higher. In conclusion, the optimization scheme proposed in this study can effectively improve the stability and control accuracy of the multi-motor system.

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“…Formula ( 5) is the calculation of PMSM1 compensation error with the introduction of speed comprehensive evaluation index ω p [18].…”

Section: Plos Onementioning

confidence: 99%

Research on coupling control of multiple permanent magnet synchronous motors based on NAISMC and SMDO

Hao,

Zhao

2023

PLoS ONE

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“…Achieving high quality control of hydraulic systems under the influence of numerous uncertainties has been a hot research topic in both academia and industry 10 . To cope with the parameters uncertainties and unmodeled dynamics of hydraulic systems, the popular control algorithms can be broadly classified into four categories, the first is observer‐based control, 11–15 the second is neural network or fuzzy control, 16–19 the third is parameter adaptive‐based control, 20–27 the fourth is the combination of these methods 28,29 …”

Section: Introductionmentioning

confidence: 99%

“…Achieving high quality control of hydraulic systems under the influence of numerous uncertainties has been a hot research topic in both academia and industry. 10 To cope with the parameters uncertainties and unmodeled dynamics of hydraulic systems, the popular control algorithms can be broadly classified into four categories, the first is observer-based control, [11][12][13][14][15] the second is neural network or fuzzy control, [16][17][18][19] the third is parameter adaptive-based control, [20][21][22][23][24][25][26][27] the fourth is the combination of these methods. 28,29 In general, observer-based control algorithms regard parameters uncertainties and external disturbances as the "total disturbance," 12,13,15 the advantages are that the algorithm is simple and can be implemented easily, but the shortcoming is not detailed enough in dealing with parameters uncertainties, and it may add additional burden to the observer if system has large parameters uncertainties.…”

mentioning

confidence: 99%

“…10 To cope with the parameters uncertainties and unmodeled dynamics of hydraulic systems, the popular control algorithms can be broadly classified into four categories, the first is observer-based control, [11][12][13][14][15] the second is neural network or fuzzy control, [16][17][18][19] the third is parameter adaptive-based control, [20][21][22][23][24][25][26][27] the fourth is the combination of these methods. 28,29 In general, observer-based control algorithms regard parameters uncertainties and external disturbances as the "total disturbance," 12,13,15 the advantages are that the algorithm is simple and can be implemented easily, but the shortcoming is not detailed enough in dealing with parameters uncertainties, and it may add additional burden to the observer if system has large parameters uncertainties. In paper 11, the uncertainties of hydraulic servo systems are divided into hydraulic dynamic uncertainty and mechanical dynamic uncertainty, and a seventh-order extended state observer was devised for the estimation and compensation of these uncertainties, however, the parameters of high-order observer are usually difficult to tune.…”

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confidence: 99%

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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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“…Meanwhile, aforementioned nonlinear control methods require full system states to be known or measurable in the backstepping designs, which is hardly available in engineering practice. Some scholars have realized the singular dynamics of hydraulic systems (Barchi et al, 2021; Guo et al, 2022; Jing et al, 2020) and have shown improved hydraulic tracking control. Notably, to the authors’ best knowledge, the above control schemes for DVPEHS systems are rarely discussed and deserve ongoing exploration for practical implementation.…”

Section: Introductionmentioning

confidence: 99%

Practical adaptive robust tracking control of the dual-valve parallel electro-hydraulic servo system with reduced-order model

Hu,

Zhang,

Fang

et al. 2023

Transactions of the Institute of Measurement and Control

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To meet the increasing demand for trajectory tracking accuracy and high-efficiency requirements in modern mobile machinery, this paper proposes a practical adaptive robust control method based on the dual-valve parallel electro-hydraulic servo system. Existing tracking control strategies for such servo systems rely on a backstepping method and assume that full states are known, which is stringent in practice. To cope with the problem, we reduce the mathematical model order of the studied system using singular perturbation theory in this paper, and only the position feedback is required for the control implementation. Then, to achieve high accuracy tracking control, a direct adaptive robust control scheme combing with a dynamic flow allocation layer is adopted to synthesize the controller. With this method, parameter uncertainties and load disturbance are rejected, and valve characteristics are considered in the dynamic flow allocation layer to solve the flow redundancy problem, respectively. Convergence of the simplified system tracking results is proved theoretically. Extensive co-simulations and experimental tests are carried out to illustrate the effectiveness of the proposed strategy, both tracking precision and flow distribution can be achieved efficiently by simple parameter adjustments in the field.

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Order-reduced-model based integral sliding mode control of a heavy-duty hydraulic manipulator with disturbances estimation

Cited by 5 publications

References 21 publications

Research on coupling control of multiple permanent magnet synchronous motors based on NAISMC and SMDO

Research on coupling control of multiple permanent magnet synchronous motors based on NAISMC and SMDO

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

Practical adaptive robust tracking control of the dual-valve parallel electro-hydraulic servo system with reduced-order model

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