An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot

Xie, Shane; Jamwal, Prashant K.

doi:10.1016/j.eswa.2010.12.154

Cited by 79 publications

(48 citation statements)

References 21 publications

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“…2 point out that the dead-zone phenomenon is very small and can be eliminated. From the literature review ( [24], [27], [37]), the pressure dynamics of the system can be then derived as follows:…”

Section: Problem Statement and Grey-box Modelmentioning

confidence: 99%

An Integrated Intelligent Nonlinear Control Method for a Pneumatic Artificial Muscle

Dinh

Ahn

2016

IEEE/ASME Trans. Mechatron.

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Section: Problem Statement and Grey-box Modelmentioning

confidence: 99%

An Integrated Intelligent Nonlinear Control Method for a Pneumatic Artificial Muscle

Dinh

Ahn

2016

IEEE/ASME Trans. Mechatron.

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“…Independently using PID tuning algorithms is not possible due to the responses being nonlinear. Therefore, intelligent control strategies have been researched and widely used [2,26,28]. Neural PID is a typical one.…”

Section: Itroductionmentioning

confidence: 99%

Iterative learning control method for improving the effectiveness of upper limb rehabilitation

Miao

Xie

et al. 2016

2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

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Abstract-In rehabilitation, passive control mode is common used at early stages of the post-stroke therapy, when the impaired limb is usually unresponsive. The simplest is the use of a proportional-integral-derivative (PID) feedback control which usually regulates the position or the interaction force along a known reference. Nonetheless PID method cannot achieve an ideal tracking performance due to dynamical uncertainties and unknown time-varying periodic disturbances from the environment. In order to minimize steady-state error with respect to uncertainties in exoskeleton passive control, Iterative Learning Control(ILC) and Neural PID control are proposed to improve the control effective of conventional linear PID. In this paper, two different control algorithms are introduced. Moreover, an experimental study on a 5-DOF upper limb exoskeleton with them is addressed for comparison.

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“…The actuator has an outstanding power-to-weight-ratio, natural compliance as well as pneumatic related advantages, such as compactness, cleanliness and simplicity [14]. Recent progress in pneumatic components, such as high speed solenoid and servo valves, as well as novel control strategies have brought about an increasing dissemination of the muscles [15,16]. However, few current work has presented the applications of PAMs in wrist robots.…”

Section: Introductionmentioning

confidence: 99%

Design and control of a robotic wrist orthosis for joint rehabilitation

Meng

Sheng

Klinger

et al. 2015

2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

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Abstract-Ageing society in many countries has led to an increasing number of stroke and cerebral palsy patients who require rehabilitation therapy. Affected wrist joints often show an increased spasticity and stiffness, caused by impairments of surrounding muscles and tendons. However, the medical devices for wrist joint assessment and rehabilitation are lacking. This paper proposes a robotic orthosis to assist the patient's wrist to perform rehabilitation exercise in a compliant way. A 1-DOF robotic device with parallel mechanism is designed for the wrist joint by utilising pneumatic artificial muscles (PAMs) that are compliant and lightweight. The mechanical design of the wrist orthosis and the corresponding development of pneumatic control system will be also presented. A model-based pressure close-loop control strategy is implemented for the PAMs in order to track the trajectory in high-performance. Experiments on the orthosis demonstrated that the robot could assist the hand to move along a torque-sensitive trajectory with relatively small errors and the differential forces were also kept stable.

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An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot

Cited by 79 publications

References 21 publications

An Integrated Intelligent Nonlinear Control Method for a Pneumatic Artificial Muscle

An Integrated Intelligent Nonlinear Control Method for a Pneumatic Artificial Muscle

Iterative learning control method for improving the effectiveness of upper limb rehabilitation

Design and control of a robotic wrist orthosis for joint rehabilitation

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