Feedforward Motion Control With a Variable Stiffness Actuator Inspired by Muscle Cross-Bridge Kinematics

Chang, Handdeut; Kim, Sangjoon J.; Kim, Jung

doi:10.1109/tro.2019.2900567

Cited by 16 publications

(9 citation statements)

References 36 publications

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“…Motivated by motor learning skill of human from this research, we can simply modulate joint stiffness of robot joint like human and realize the stable motion (Chang et al, 2017). simply updating the ratio of joint stiffness, without complicated and heavy load calculation of body dynamics and optimization in each instant during motion (Chang et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Human elbow motor learning skills of varying loads: Proof of internal model generation using joint stiffness estimation

Shin

Chang

Kim

2021

JBSE

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This study presents a human elbow motor learning strategy responding to varying loads. Inspired by Kawato's internal model theory, we suggest hypothesis that human minimize the internal model error by updating the joint stiffness to generate stable and robust motion during repetitive voluntary action with varying weight of load condition. We designed experimental robotics device to verify our hypothesis and the device is capable of precisely measuring human elbow joint stiffness very accurately. The subject was instructed to perform the prescribed elbow motion without notifying the weight of the load for neutral experimental condition and we recorded joint position, perturbation torque of actuator, reaction torque from torque sensor, and mean absolute value (MAV) of the surface EMG (sEMG) in forearm muscles and upper arm muscles as a reference criterion for elbow joint impedance modulation during motor learning. Modified ensemble-based system identification was applied to characterize the dynamic elbow mechanical impedance in transient state of moving loads. Experimental results show that subjects utilized high joint stiffness initially, but it decreases gradually and saturated to the level of 20%~60% of initial value after repetitive motion tests. The degree of saturation of motor learning varied with the weight of loads, this result supports the hypothesis that motor learning reduces joint stiffness by providing accurate internal model.

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Section: Discussionmentioning

confidence: 99%

Human elbow motor learning skills of varying loads: Proof of internal model generation using joint stiffness estimation

Shin

Chang

Kim

2021

JBSE

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“…which is equivalent to θ 1 − θ 2 = σ d , due to (16). By solving for η 1 and η 2 with ( 26) and ( 27), the equilibrium of virtual motor coordinates η 1,d and η 2,d are now written as functions of σ d and…”

Section: B Setting Motor Pretension By σ D and Q Dmentioning

confidence: 99%

“…As a consequence, numerous methods were developed for controlling such robots with elastic joints in terms of regulation or tracking tasks, e.g., PD-regulator with gravity compensation [11] [12], feedback linearization [13] [14], backstepping [15]. Recently, control methods based on inverse dynamics [16] and linear quadratic regulator [17] have been developed for VSA that allow the active variation of the mechanical stiffness. In [18] [19], passivity-based control laws are proposed and showed excellent performance for the impedance control on the robots with relatively stiff joints.…”

Section: Introductionmentioning

confidence: 99%

Elastic Structure Preserving Impedance Control of Bidirectional Antagonistic Variable Stiffness Actuation

Meng

Keppler

Ott

2021

2021 European Control Conference (ECC)

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We propose an impedance controller for articulated soft robots implemented by bidirectional antagonistic variable stiffness (BAVS) actuation, where two motors are connected via nonlinear elastic elements to a single link allowing for a joint stiffness modulation. Naturally, the highly elastic elements introduce undesired oscillatory dynamics into the plant. To address this problem, we present a controller that allows to impose a desired link-side stiffness and damping behavior while preserving the intrinsic inertial elastic properties of the system. This allows us to solve the global asymptotic regulation problem while simultaneously imposing a desired joint stiffness preset on the BAVS actuator. We provide a passivity and stability analysis based on a physically motivated storage and Lyapunov function. Experimental results on the underarm BAVS joint of DLR David validate our control law.

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“…The nonlinear model predictive controller [29] has been used for the position tracking control of the two link planar robot driven by antagonistic VSJs. A feedforward motion controller has been designed for the stiffness control of the VSJ developed using variable radius gear transmission inspired by biological musculoskeletal system [30].…”

Section: Introductionmentioning

confidence: 99%

Robust Tracking Control of Variable Stiffness Joint Based on Feedback Linearization and Disturbance Observer With Estimation Error Compensation

Guo

2020

IEEE Access

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The variable stiffness joint (VSJ) has the characteristics of independent and controllable position and stiffness. The variable stiffness characteristics and inherent flexibility make the VSJ suitable to be used as the actuation joint of the physical human-robot interaction application robot, so as to improve the task adaptability of the robot and physical human-robot interaction safety. The VSJ based on equivalent lever mechanism has the advantages of low energy consumption in stiffness adjustment, so there are many researches on this type of VSJ. The tracking control of output link angular position and joint output stiffness are two basic control targets of the VSJ. For the system dynamic model of the VSJ based on equivalent lever mechanism, considering the unknown parametric perturbations, the unknown friction torques acting on the drive units, the unknown external disturbance acting on the output link and the control input saturation constraints, a robust tracking controller based on feedback linearization, disturbance observer with antiwindup measures, sliding mode control and estimation error compensator is designed to improve the tracking control accuracy of the position and stiffness of the VSJ. The simultaneous tracking control of position and stiffness of the VSJ can be achieved by the designed controller, and the simulation results show the effectiveness and robustness of the proposed controller. Moreover, the simulation results show that the proposed estimation error compensator for the disturbance observer with fixed preset observation gain can effectively reduce the system output tracking error and improve the anti-disturbance characteristics of the controller.

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Feedforward Motion Control With a Variable Stiffness Actuator Inspired by Muscle Cross-Bridge Kinematics

Cited by 16 publications

References 36 publications

Human elbow motor learning skills of varying loads: Proof of internal model generation using joint stiffness estimation

Human elbow motor learning skills of varying loads: Proof of internal model generation using joint stiffness estimation

Elastic Structure Preserving Impedance Control of Bidirectional Antagonistic Variable Stiffness Actuation

Robust Tracking Control of Variable Stiffness Joint Based on Feedback Linearization and Disturbance Observer With Estimation Error Compensation

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