A Smoothed GMS friction model suited for gradient-based friction state estimation

Boegli, Max; Laet, Tinne De; Schutter, Joris De; Swevers, Jan

doi:10.1109/acc.2012.6315360

Cited by 15 publications

(9 citation statements)

References 13 publications

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“…In [28], a modified Maxwell-slip (MMS) model is developed for identifying the asymmetric hysteresis of a class of piezo-actuators. The MMS model involves the typical switching structure of the Maxwell-slip form that makes the online parameter identification difficult, as pointed out in [29]. A total of 42 parameters are involved in the general MMS friction model in [28], whereas 40 parameters characterize the Prandt-Ishlinskii model of a PAM in [17].…”

Section: Identification Results and Discussionmentioning

confidence: 99%

A Multistate Friction Model for the Compensation of the Asymmetric Hysteresis in the Mechanical Response of Pneumatic Artificial Muscles

et al. 2019

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These days, biomimetic and compliant actuators have been made available to the main applications of rehabilitation and assistive robotics. In this context, the interaction control of soft robots, mechatronic surgical instruments and robotic prostheses can be improved through the adoption of pneumatic artificial muscles (PAMs), a class of compliant actuators that exhibit some similarities with the structure and function of biological muscles. Together with the advantage of implementing adaptive compliance control laws, the nonlinear and hysteretic force/length characteristics of PAMs pose some challenges in the design and implementation of tracking control strategies. This paper presents a parsimonious and accurate model of the asymmetric hysteresis observed in the force response of PAMs. The model has been validated through the experimental identification of the mechanical response of a small-sized PAM where the asymmetric effects of hysteresis are more evident. Both the experimental results and a comparison with other dynamic friction models show that the proposed model could be useful to implement efficient compensation strategies for the tracking control of soft robots.

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Section: Identification Results and Discussionmentioning

confidence: 99%

A Multistate Friction Model for the Compensation of the Asymmetric Hysteresis in the Mechanical Response of Pneumatic Artificial Muscles

et al. 2019

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“…11 In addition to the above two famous models, the generalized Maxwell-slip (GMS) model was developed to predict the friction behavior in presliding regime for control purposes. 12 Over the past few years, due to the ability to approximate arbitrary nonlinear systems, neural network has been widely explored and attempted to model the nonlinear friction. A comprehensive literature review on this subject can be found in Bai and Pu 13 The Elman neural network (ENN), 14 which possesses dynamic characteristics, is employed to model the nonlinear friction of hydraulic cylinder in this work.…”

Section: Introductionmentioning

confidence: 99%

New approach of friction model and identification for hydraulic system based on MAPSO-NMDS optimization Elman neural network

Pan

et al. 2017

Advances in Mechanical Engineering

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Hydraulic system smoothness is critical to product quality and position tracking. However, the nonlinear friction is an important factor causing unstable phenomenon of the hydraulic system when at low speed. Relevant research shows that the identified nonlinear friction has a large error with the actual friction force, which will bring a great challenge to design the friction compensation controller. Considering the difficulty of the friction model and identification, this article presents a new approach of friction model and identification for hydraulic system based on multi-agent particle swarm optimization-Nelder-Mead downhill simplex algorithm optimization Elman neural network. First, the nonlinear friction was established using the Elman neural network model. Then, a multi-agent particle swarm optimization-Nelder-Mead downhill simplex algorithm was proposed to search the nonlinear parameters of the Elman neural network model. Subsequently, the proposed algorithm was validated by complex nonlinear functions. Finally, the effectiveness of the Elman neural network model was demonstrated by the results from experiment. And comparison with the conventional LuGre friction model, the results show that the Elman neural network model predicts friction more accurately. This study provides the bases for designing the friction compensation controller.

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“…The LuGre model describes the asperities between two contact surfaces by elastic bristles, and true nature of dynamic friction is considered as the result of the average deflection of these bristles. In advance of the LuGre model, the Leuven model [12,13], and GMS model, authors of [14,15,19] modeled the hysteresis behavior of the dynamic friction within presliding regime. Two-state dynamic friction model [16] was also proved to be capable of capturing the hysteresis behavior.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Friction Parameter Identification Method with LuGre Model for Direct-Drive Rotary Torque Motor

Wang

Lin

Wang

2016

Mathematical Problems in Engineering

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Attainment of high-performance motion/velocity control objectives for the Direct-Drive Rotary (DDR) torque motor should fully consider practical nonlinearities in controller design, such as dynamic friction. The LuGre model has been widely utilized to describe nonlinear friction behavior; however, parameter identification for the LuGre model remains a challenge. A new dynamic friction parameter identification method for LuGre model is proposed in this study. Static parameters are identified through a series of constant velocity experiments, while dynamic parameters are obtained through a presliding process. Novel evolutionary algorithm (NEA) is utilized to increase identification accuracy. Experimental results gathered from the identification experiments conducted in the study for a practical DDR torque motor control system validate the effectiveness of the proposed method.

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A Smoothed GMS friction model suited for gradient-based friction state estimation

Cited by 15 publications

References 13 publications

A Multistate Friction Model for the Compensation of the Asymmetric Hysteresis in the Mechanical Response of Pneumatic Artificial Muscles

A Multistate Friction Model for the Compensation of the Asymmetric Hysteresis in the Mechanical Response of Pneumatic Artificial Muscles

New approach of friction model and identification for hydraulic system based on MAPSO-NMDS optimization Elman neural network

Dynamic Friction Parameter Identification Method with LuGre Model for Direct-Drive Rotary Torque Motor

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