Frequency-division based hybrid force/position control of robotic arms manipulating in uncertain environments

Liu, Guanghui; Fang, Lijin; Han, Bing; Zhang, Hualiang

doi:10.1108/ir-11-2019-0228

Cited by 10 publications

(6 citation statements)

References 16 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fortunately, the delay is insignificant for interactive manipulation of the robot [24]. Besides, stroke patients move their arms very slowly [16], so the responding time for the rehabilitation robot to human intention is not rigorous. And the delay might be eliminated through the use of EMG to estimate the active human torque based on the musculoskeletal model in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Soltani et al used a hybrid force/position control to apply a constant force while tracking the desired trajectory for a soft robot [15]. Liu et al introduced a hybrid force/position control based on the stiffness estimation of the unknown environment for robotic arms, and attained accurate control and stable system [16]. Chu et al proposed a hybrid force/position control for a redundant parallel mechanism, and found its significant reduction in internal force for the mechanism [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

et al. 2020

View full text Add to dashboard Cite

Rehabilitation robots play an increasingly important role in the recovery of motor function for stroke. To ensure a natural physical human-robot interaction (pHRI) and enhance the active participation of subjects, it is necessary for the robots to understand the human intention and cooperate actively with humanlike characteristics. This study proposed a hybrid active control algorithm with human motion intention detection. The motion intention was defined as the desired position and velocity, which were continuously estimated according to the human upper-limb model and minimum jerk model, respectively. The motion intention was then fed into a hybrid force and position controller of an upper-limb cable driven rehabilitation robot (CDRR). And a three-dimensional reaching task without predefined trajectory was employed to validate the effectiveness of the proposed control algorithm. Experimental results showed that the control algorithm could continuously recognize the human motion intention and enabled the robot better movement performance indicated as smaller offset error, smoother trajectory, and lower impact. The proposed method could guarantee a natural pHRI and improve the engagement of the subjects, which has great potential in clinical applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Liu et al [99] developed a hybrid force/position control system for robotic arms based on the stiffness estimation of an unknown environment, resulting in precise control and a stable system. To increase the robot's applicability and dependability in welding, polishing, and assembling, a frequency-division control method is devised.…”

Section: Control Laws Used With Motion Intention Estimation In Power ...mentioning

confidence: 99%

Power Assist Rehabilitation Robot and Motion Intention Estimation

Adeola-Bello¹,

Azlan²

2022

IJRCS

View full text Add to dashboard Cite

This article attempts to review papers on power assist rehabilitation robots, human motion intention, control laws, and estimation of power assist rehabilitation robots based on human motion intention in recent years. This paper presents the various ways in which human motion intention in rehabilitation can be estimated. This paper also elaborates on the control laws for the estimation of motion intention of the power assist rehabilitation robot. From the review, it has been found that the motion intention estimation method includes: Artificial Intelligence-based motion intention and Model-based motion intention estimation. The controllers include hybrid force/position control, EMG control, and adaptive control. Furthermore, Artificial Intelligence based motion intention estimation can be subdivided into Electromyography (EMG), Surface Electromyography (SEMG), Extreme Learning Machine (ELM), and Electromyography-based Admittance Control (EAC). Also, Model-based motion intention estimation can be subdivided into Impedance and Admittance control interaction. Having reviewed several papers, EAC and ELM are proposed for efficient motion intention estimation under artificial-based motion intention. In future works, Impedance and Admittance control methods are suggested under model-based motion intention for efficient estimation of motion intention of power assist rehabilitation robot. In addition, hybrid force/position control and adaptive control are suggested for the selection of control laws. The findings of this review paper can be used for developing an efficient power assist rehabilitation robot with motion intention to aid people with lower or upper limb impairment.

show abstract

“…Nowadays, robotic manipulators are presenting in different fields, such as space exploration, 1 surgical robot, 2 industrial application. 3 In order to meet the the requirements of control performance, various advanced control technologies could be applied to the controllers of robotic manipulators, e.g., robust control, 4 sliding mode control, 5 fuzzy control, 6 neural network control. 7,8 However, the accuracy of tracking control for robotic manipulator is affected by backlash hysteresis and system uncertainties (e.g., disturbance, nonlinear friction and coupled terms) which are not easily obtained in advance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural sliding mode control with prescribed performance of robotic manipulators subject to backlash hysteresis

Wang

Fang

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

Self Cite

View full text Add to dashboard Cite

Robust and precise control of robot systems are still challenging problems due to the existence of uncertainties and backlash hysteresis. To deal with the problems, an adaptive neural sliding mode control with prescribed performance is proposed for robotic manipulators. A finite-time nonsingular terminal sliding mode control combined with a new prescribed performance function (PPF) is developed to guarantee the transient and steady-state performance of the closed-loop system. Based on the sliding mode variable, an adaptive law is presented to effectively estimate the bound of system uncertainties where the prior knowledge of uncertainties is not needed. To approximate nonlinear function and unknown dynamics, the Gaussian radial basis function neural networks(RBFNNs) is introduced to compensate the lumped nonlinearities. All signals of the closed-loop system are proven to be uniformly ultimately bounded (UUB) by Lyapunov analysis. Finally, comparative simulations are conducted to illustrate superiority and reliability of the proposed control strategy.

show abstract

Frequency-division based hybrid force/position control of robotic arms manipulating in uncertain environments

Cited by 10 publications

References 16 publications

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

Power Assist Rehabilitation Robot and Motion Intention Estimation

Adaptive neural sliding mode control with prescribed performance of robotic manipulators subject to backlash hysteresis

Contact Info

Product

Resources

About