Adaptive robust decoupling control of multi-arm space robots using time-delay estimation technique

Zhang, Xin; Liu, Jinguo; Gao, Qing; Ju, Zhaojie

doi:10.1007/s11071-020-05615-5

Cited by 57 publications

(26 citation statements)

References 35 publications

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“…where L = TÀU, Q is dissipative energy, C is the constraint equation, l is the Lagrange generator corresponding to the constraint, F is the generalized external force. By substituting equations (12), (13), and (14) into equation (15), the dynamic differential equation of flexible body follows:…”

Section: Flexible Component Analysismentioning

confidence: 99%

“…The calculated torque controller (CTC) is widely used in the controller design of industrial robot and manipulator. 15 An internal control loop is introduced into the dynamic control of the robot, whose function is to dynamically compensate the system. As shown in Figure 6, the traditional CTC combines the calculation of nonlinear force term V ( q , q · ) and gravity term G ( q ) in equation (11) and PD control into the controller to realize the decoupling and linearization of the system.…”

Section: Dynamic Decoupling and Controlmentioning

confidence: 99%

“…13,14 In the dynamic model, using CTC as dynamic compensation can make the robot become a more easily controlled system. Zhang et al 15 designed an adaptive robust decoupling controller based on time-delay estimation (TDE) and sliding mode control (SMC) for a multi-arm space robot. The dynamic coupling of the robot was compensated by TDE technology, then SMC complemented and reinforced the robustness of the TED.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic analysis and decoupled control of a heavy-duty walking robot with flexible feet based on super twisting algorithm

Ren

Zhang

2020

Measurement and Control

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In this paper, the dynamics and control strategies of a biped robot with 6-DOF parallel leg mechanism are studied. Firstly, the multi-body kinematic model and dynamic model of the robot are established. Secondly, the insufficient stiffness of robot’s feet and the coupling effect between the kinematic chains are considered in dynamics modeling, and the rigid-flexible coupling model is established by using ADAMS and finite element method. Finally, aiming at the position deviation and system vibration caused by the flexible foot, a strategy based on the combination of a computed torque controller (CTC) and a second-order sliding-mode super twisting algorithm (STA) is designed. At the same time, the control strategy based on CTC and PID and the control strategy based on CTC and sliding mode control (SMC) are designed to compare with CTC-STA. The results show that CTC-STA has faster regulation ability and stronger robustness than CTC-SMC and CTC-PID.

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Section: Flexible Component Analysismentioning

confidence: 99%

Section: Dynamic Decoupling and Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic analysis and decoupled control of a heavy-duty walking robot with flexible feet based on super twisting algorithm

Ren

Zhang

2020

Measurement and Control

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“…Finally, it can achieve the expected motion control. 6 Therefore, researchers often use surface electromyography (sEMG), which is easily collected from the forearm of the human body, as the external control source of dexterous hands, and analyze and process it and gesture recognition, but the recognition accuracy still needs further improved. 7,8 In order to enable the bionic dexterous hand to effectively control the sEMG accurately, the traditional machine learning needs to preprocess the raw sEMG and use various modern algorithms to extract the time domain or frequency domain features.…”

Section: Introductionmentioning

confidence: 99%

“…Dexterous hand needs the input of an external signal source, and after a series of processing of controlling commands. Finally, it can achieve the expected motion control 6 . Therefore, researchers often use surface electromyography (sEMG), which is easily collected from the forearm of the human body, as the external control source of dexterous hands, and analyze and process it and gesture recognition, but the recognition accuracy still needs further improved 7,8 .…”

Section: Introductionmentioning

confidence: 99%

Gesture recognition based on surface electromyography‐featureimage

Cheng

et al. 2020

Concurrency and Computation

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For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1-dim convolution kernel to extract deep abstract features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw-sEMG images based on the CNN and the sEMG-feature-images based on the CNN and sEMG based on the traditional machine learning, the multi-sEMG-features image based on the CNN is the highest, which coming up to 82.54%.

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Tracking control of an uncertain heavy load robot based on super twisting sliding mode control and fuzzy compensator

Ren

Zhang

2021

Asian Journal of Control

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This paper presents a new combined strategy to control an uncertain heavy load robot with position disturbance caused by flexible foot, and the control strategy is realized only by position feedback. Firstly, deformation during motion is analyzed, and the position transformation generated by this deformation is treated as periodic position disturbance and non‐periodic position disturbance. Then, a control method combined by computed torque controller (CTC), super twisting sliding mode (STSM) controller, and fuzzy compensator is designed for the robot dynamic system to track ideal trajectory. Meanwhile, a proportional–integral–derivative (PID) controller based on CTC is designed as a comparison to verify the trajectory tracking performance and robustness of the combined strategy under external disturbances. Finally, simulation results show that CTC‐STSM is clearly found to converge faster and to show stronger ability to suppress interference than CTC‐PID. The addition of fuzzy compensator (CTC‐STSM‐Fuzzy) can further reduce the sliding mode chattering effect and improve the performance.

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Adaptive robust decoupling control of multi-arm space robots using time-delay estimation technique

Cited by 57 publications

References 35 publications

Dynamic analysis and decoupled control of a heavy-duty walking robot with flexible feet based on super twisting algorithm

Dynamic analysis and decoupled control of a heavy-duty walking robot with flexible feet based on super twisting algorithm

Gesture recognition based on surface electromyography‐featureimage

Tracking control of an uncertain heavy load robot based on super twisting sliding mode control and fuzzy compensator

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