Position control of electro-hydraulic servo system using active disturbance rejection control for upper-limb exoskeleton

Tang, Jing; Cao, Jiaxun; Wu, Minghu; Lei, Z. H.; Zhang, Fan

doi:10.21595/jve.2022.22850

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…In some recent studies in the literature, it is seen that only PID controllers [28][29][30][31] control robot arms. Again, in some recent studies in the literature, it is seen that the data obtained from EMG muscle signals only work with fuzzy controllers or machine learning algorithms in this direction [32][33][34][35][36][37]. Many studies have compared EMG signal data with different control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Ersin,

Yaz

2024

Journal of Sensors

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In this study, a wearable exoskeleton arm was designed and controlled with different control methods to help people with muscle disorders in their arms and support treatment. The developed robot arm was transferred to Simulink software with the Simmechanics application. Two electromyography (EMG) muscle sensors and the ADXL335 position and acceleration sensors attach to the human arm’s biceps and triceps muscle areas. As the human moved the arm, data were obtained from the EMG muscle sensors and the ADXL335 position and acceleration sensor. The received data were first trained with the fuzzy logic algorithm. The same data were then trained with machine learning algorithms in Simulink software. It has been determined that the best result is the quadratic support vector machine (SVM) algorithm. The fuzzy logic algorithm trained with the PID controller block and the received sensor data have been added to the degrees of freedom regions that will enable rotation in the block diagram of the previously exported system. Later, the fuzzy logic block was removed and the machine learning algorithm, the quadratic SVM algorithm, was added. The designed system was operated with two different control systems, and the control algorithm closest to the human arm movement was determined. In addition, each part of the system, whose design was prepared, was removed and assembled separately with a 3D printer. ESP32 microcontroller development board was used to control the system, and it was run in real-time with EMG muscle sensors and position sensors.

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Section: Introductionmentioning

confidence: 99%

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Ersin,

Yaz

2024

Journal of Sensors

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“…With the development trend of high pressure, high power, variable pressure, and intelligence of hydraulic systems in aerospace, robotics, construction machinery, and other fields, the distributed hydraulic control system will be widely used in the future [1][2][3]. In the aviation field, the new EHA (Electro-hydrostatic actuator) has been used to replace the traditional hydraulic actuator in the airborne hydraulic actuator system [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Design and Characteristic Research on Variable Displacement Mechanism of Two-Dimensional (2D) Bivariable Pump

Xing,

Ji,

Yang

et al. 2024

Energies

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In a hydraulic system, a micro variable pump is required to be high pressure and high speed, and this work presents a new type of 2D bivariable pump structure in which the worm gear and worm mechanism are used to rotate the cylinder block to change the flow distribution state of the cylinder window and the piston groove to change the displacement of the 2D pump. The flow–pressure mathematical model of the 2D variable pump is established to analyze the relationship between pump displacement and the pump cylinder rotation angle and the effects of variable displacement on pump pressure characteristics, flow characteristics, and volume efficiency in Matlab. During the experiment, we tested the change in the corresponding pump output flow when the cylinder rotation angle is 0~12°, which verifies the correctness of the variable calculation model. The experimental results indicate that the volume efficiency and mechanical efficiency of the single-piston 2D pump are reduced to different degrees after variable displacement, the volume efficiency is reduced by approximately 3% at most, and the mechanical efficiency is reduced by approximately 5% at most.

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Model reference adaptive control of electro-hydraulic servo system based on RBF neural network and nonlinear disturbance observer

Zhong,

Liu,

Qiang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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For the electro-hydraulic servo systems (EHSS) subjected to parameter uncertainties and unknown load disturbances, a model reference adaptive controller (MRAC) is proposed in this paper. Based on radial basis function neural networks (RBF NN) and nonlinear disturbance observer (NDO), it ensures a high performance in tracking output to the reference model. Firstly, a nominal MRAC is developed using Liapunov theory. In addition, a RBF NN is constructed to approximate parameter uncertainty and other nonlinear functions online. Then, the NDO is devised to estimate the nonlinear terms containing unknown load disturbances and compensate for disturbance. Besides, the stability of the closed-loop system is analyzed. Finally, the proposed controller is simulated and experimentally verified. According to the simulation results, the control method proposed in this paper is advantageous over other controllers in improving the accuracy of position tracking and enhancing the robustness of the system. Moreover, the superiority of this control method is demonstrated by the experimental results.

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Position control of electro-hydraulic servo system using active disturbance rejection control for upper-limb exoskeleton

Cited by 3 publications

References 22 publications

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Design and Characteristic Research on Variable Displacement Mechanism of Two-Dimensional (2D) Bivariable Pump

Model reference adaptive control of electro-hydraulic servo system based on RBF neural network and nonlinear disturbance observer

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