Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles

Jiang, Xuepeng; Wang, Zenghuai; Zhang, Chao; Liu, Yang

doi:10.1108/ir-07-2014-0374

Cited by 27 publications

(13 citation statements)

References 20 publications

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“…Simple nonlinear control techniques such as robust torque control scheme (14, 15) and impedance control scheme (15, 18) cannot meet the requirement under uncertain dynamics. Many other control schemes have been presented such as fuzzy adaption (19) and adaptive control schemes (17, 18), whereas these control schemes perform well for industrial robots but not for rehabilitation robots due to uncertainties and disturbances in rehabilitation training (20). Sliding mode control (SMC) is a variable structure control method, which has inherent insensitivity and robustness against uncertainties and disturbances.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Tracking Control of a Wire-Driven Upper-Limb Rehabilitation Robot with Nonlinear Disturbance Observer

et al. 2017

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Robot-aided rehabilitation has become an important technology to restore and reinforce motor functions of patients with extremity impairment, whereas it can be extremely challenging to achieve satisfactory tracking performance due to uncertainties and disturbances during rehabilitation training. In this paper, a wire-driven rehabilitation robot that can work over a three-dimensional space is designed for upper-limb rehabilitation, and sliding mode control with nonlinear disturbance observer is designed for the robot to deal with the problem of unpredictable disturbances during robot-assisted training. Then, simulation and experiments of trajectory tracking are carried out to evaluate the performance of the system, the position errors, and the output forces of the designed control scheme are compared with those of the traditional sliding mode control (SMC) scheme. The results show that the designed control scheme can effectively reduce the tracking errors and chattering of the output forces as compared with the traditional SMC scheme, which indicates that the nonlinear disturbance observer can reduce the effect of unpredictable disturbances. The designed control scheme for the wire-driven rehabilitation robot has potential to assist patients with stroke in performing repetitive rehabilitation training.

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Section: Introductionmentioning

confidence: 99%

Sliding Mode Tracking Control of a Wire-Driven Upper-Limb Rehabilitation Robot with Nonlinear Disturbance Observer

et al. 2017

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“…When applying the model-based control theory, the first step is to model the plant and then to do the designing of the controller based on the plant model. Some of the MBC algorithms include adaptive position control (Zhu et al, 2008), sliding mode control (Cao, Xie & Das, 2018), adaptive backstepping control (Chang, 2010), switching model control (Jiang et al, 2015), nonlinear optimal predictive control (Todorov et al, 2010) and active modelbased control (Bleicher et al, 2011). For the DDC method, the controller is designed directly using online or offline input/output data of the controlled system without employing the mathematical model of the controlled plant.…”

Section: Introductionmentioning

confidence: 99%

“…For the DDC method, the controller is designed directly using online or offline input/output data of the controlled system without employing the mathematical model of the controlled plant. Examples of DDC algorithms are PID control (Fan et al, 2015), neural network nonlinear control (Chiang & Chen, 2017), fuzzy control (Jiang et al, 2015), model-free adaptive control (Ahmed, Wang & Yang, 2018) and data-driven predictive control. Although MBC ensures a higher positioning precision for PAM applications compared to DDC, its design process requires that the system be modelled and that there be an adequate knowledge of control theory, making it difficult for engineers who are unacquainted with the modeling and controller design to implement and make the controller impracticable for realtime systems.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Data-Driven Predictive Controller for an Artificial Muscle

Kaplanoğlu¹,

Akgün²,

Ülkir³

2019

STUD INFORM CONTROL

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This study presents a position tracking control method, with reference to Data-Driven Predictive Controller (DDPC), for a Pneumatic Artificial Muscle (PAM) system. The design of predictive controller is created from the subspace identification matrices acquired by input/output data. The control scheme is entirely data-based without explicit use of a model in the control application that can rectify the nonlinearity and uncertainties of the PAM. Firstly, subspace matrices are developed employing the identification method as a predictor by using open-loop experiments. Secondly, the estimated subspace matrices are used to design the so-called DDPC. In this instance, the quadratic programming (QR) decomposition method is used to obtain the prediction matrices. Consequently, experiments are carried out by the PAM actuator with different testing and loading conditions. The real-time experimental results demonstrate the feasibility and efficiency of the suggested control approach for nonlinear systems.

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“…Owing to its particular structure and materials, PAM has several unique advantages compared to hydraulic and electric actuators, for instance, high power-toweight ratio, compact size, inherent compliance, as well as low cost [3] and [4]. Therefore, PAMs are increasingly used in industrial applications [5] to [7], bionic robots [8] to [10], surgical instruments [11], rehabilitation devices [12] to [14], and so forth.…”

Section: Introductionmentioning

confidence: 99%

Modelling Length/Pressure Hysteresis of a Pneumatic Artificial Muscle using a Modified Prandtl-Ishlinskii Model

Liu

Zang

Lin

et al. 2017

SV-JME

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Pneumatic artificial muscles have been widely used in various fields owing to their inherent compliance and high power-to-weight ratio. However, the natural hysteresis nonlinearity including length/pressure hysteresis and force/pressure hysteresis degrades their performance in precise tracking control, making it necessary to build a mathematical hysteresis model for hysteresis compensation. This paper deals with the modelling of length/pressure hysteresis of pneumatic artificial muscles. The length/pressure hysteresis loops measured by the isotonic test are found to be asymmetric and independent of the external load when the load is small. Considering that the classical Prandtl-Ishlinskii model is only effective for symmetric hysteresis, a modified Prandtl-Ishlinskii model is proposed to describe the length/pressure hysteresis behaviour. The developed model utilizes two asymmetric operators with simple mathematical forms to independently model the ascending branch and descending branch of hysteresis loops. The model parameters are identified using the recursive least square algorithm. Comparisons between simulation results and experimental measurements demonstrate that the proposed model can characterize the asymmetric major hysteresis loop and minor hysteresis loops with high accuracy. Keywords: asymmetric hysteresis, length/pressure hysteresis, modified Prandtl-Ishlinskii model, pneumatic artificial muscles, recursive least square algorithm Highlights • The length/pressure hysteresis of a single pneumatic artificial muscle is found to be asymmetric and independent of small external load through experimental measurements. • This paper proposes a modified Prandtl-Ishlinskii model composing of two independent asymmetric play operators to characterize the length/pressure hysteresis of the pneumatic artificial muscle. • The parameters of the modified Prandtl-Ishlinskii model is identified quite conveniently using the recursive least mean algorithm. • The proposed model has high accuracy in describing the major hysteresis loop and minor hysteresis loops.

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Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles

Cited by 27 publications

References 20 publications

Sliding Mode Tracking Control of a Wire-Driven Upper-Limb Rehabilitation Robot with Nonlinear Disturbance Observer

Sliding Mode Tracking Control of a Wire-Driven Upper-Limb Rehabilitation Robot with Nonlinear Disturbance Observer

Real-Time Implementation of Data-Driven Predictive Controller for an Artificial Muscle

Modelling Length/Pressure Hysteresis of a Pneumatic Artificial Muscle using a Modified Prandtl-Ishlinskii Model

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