Experimental Implementations of Adaptive Self-Organizing Fuzzy Slide Mode Control to a 3-DOF Rehabilitation Robot

Chang, Ming-Kun; Yuan, Tsan-Hsiu

doi:10.1109/icicic.2008.264

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…The development and simulation of fuzzy logic based learning mechanisms related to robotic rehabilitation is well-documented, utilizing various devices to emulate human motor learning [2], [9], [10], [11], [12], [13], and [14]. The relationships between movements of upper extremities identifying joint angles using a fuzzy logic system have been reported [15]; these authors analysed a range of joint angles and rhythmical movement variables to design a fuzzy expert system. [9] utilised fuzzy inference to develop an internal model of a dynamic environment experienced during planar reaching movements with the upper limb.…”

Section: Background and Related Workmentioning

confidence: 99%

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An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation

Esfahlani¹,

Cirstea²,

Sanaei³

et al. 2017

2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)

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Rehabiliation robotics combined with video game technology provides a means of assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The current work presents ReHabGame, a serious game using a fusion of implemented technologies that can be easily used by patients and therapists to assess and enhance sensorimotor performance and also increase the activities in the daily lives of patients. The game allows a player to control avatar movements through a Kinect Xbox, Myo armband and rudder foot pedal, and involves a series of reach-grasp-collect tasks whose difficulty levels are learnt by a fuzzy interface. The orientation, angular velocity, head and spine tilts and other data generated by the player are monitored and saved, whilst the task completion is calculated by solving an inverse kinematics algorithm which orientates the upper limb joints of the avatar. The different values in upper body quantities of movement provide fuzzy input from which crisp output is determined and used to generate an appropriate subsequent rehabilitation game level. The system can thus provide personalised, autonomously-learnt rehabilitation programmes for patients with neuromuscular disorders with superior predictions to guide the development of improved clinical protocols compared to traditional theraputic activities.

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Section: Background and Related Workmentioning

confidence: 99%

“…A membership function (MF) is a curve that defines how each point in the input space is mapped to a membership value in [0, 1]. The role of the membership functions is to find the standard range for each input [4], [9], [15], [27] and [28], MF specifies a fuzzy set A as Eqn. 3, [29].…”

Section: Fuzzy Logic and Machine Learning Algorithmmentioning

confidence: 99%

An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation

Esfahlani¹,

Cirstea²,

Sanaei³

et al. 2017

2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)

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“…Pneumatic muscle (PM) actuator is very similar in action to human skeletal muscle and at a very low level of risk of injury. The main advantages of PM are safety, light weight, compliance, cheapness and high power/weight ratios (Tsagarakis and Caldwell, 2003; Chang and Yuan, 2009). It is very compatibly used in rehabilitation robots.…”

Section: Introductionmentioning

confidence: 99%

“…A challenge for the application of PM is that it can be only operated in the contractile direction. Hence, PMs have to be used in antagonistic pairs to achieve bi‐directional motion for a single joint (Noritsugu and Tanaka, 1997; Chang and Yuan, 2009; Xiong et al , 2009). Unfortunately, this arrangement requires the control of at least two actuators simultaneously.…”

Section: Introductionmentioning

confidence: 99%

RLSESN‐based PID adaptive control for a novel wearable rehabilitation robotic hand driven by PM‐TS actuators

Huang

Wang

et al. 2012

International Journal of Intelligent Computing and Cybernetics

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PurposeThe purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle‐Torsion Spring (PM‐TS) for finger therapy. PM has complex nonlinear dynamics, which makes PM modelling difficult. To realize high‐accurate tracking for the robotic hand, an Echo State Network (ESN)‐based PID adaptive controller is proposed, even though the plant model is unknown.Design/methodology/approachTo drive a single joint of rehabilitation robotic hand, the paper proposes a new PM‐TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS). Based on the novel actuator, a wearable robotic hand is designed. By employing the model‐free approximation capability of ESN, the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic hand. An ESN together with Recursive Least Square (RLS) is called a RLSESN, where the ESN output weight matrix is updated by the online RLS learning algorithm.FindingsPractical experiments demonstrate the validity of the PM‐TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID controller. In addition, they also verify the effectiveness of the proposed rehabilitation robotic hand.Originality/valueA new PM‐TS actuator configuration that uses a PM and a torsion spring for bi‐directional movement of joint is presented. By utilizing the new PM‐TS actuator, a novel wearable rehabilitation robotic hand for finger therapy is designed. Based on the unknown plant model, the RLSESN_PID controller is proposed to attain satisfactory performance.

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“…It is known that FL control has the advantages that it can provide accurate position/force with a bounded error in coping with nonlinear systems with the presence of model uncertainties. In this paper, we consider FL approaches [6], [7] to hybrid force-position control for the shoulder of the rehabilitation robot as a specified robotic arm.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Hybrid Force-Position Control for the Robotic Arm of an Upper Limb Rehabilitation Robot Powered by Pneumatic Muscles

Jiang

Chen

Sun

et al. 2010

2010 International Conference on E-Product E-Service and E-Entertainment

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The main aim of this paper is to design a hybrid forceposition controller using fuzzy logic (FL) for the robotic arm of a 9 degrees of freedom (DOF) upper limb wearable exoskeleton rehabilitation robot. The robot is designed and built in our lab for assisting in the rehabilitation of patients post-stroke. The robotic arm of the rehabilitation robot is driven by pneumatic muscles (PM) and its dynamic performance is very complex. Fuzzy logic (FL) control techniques are applied to the robotic arm and the results show that FL controller shows better performances than that of the conventional PI controller in hybrid force-position control of the specified robotic arm of the rehabilitation robot.

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Experimental Implementations of Adaptive Self-Organizing Fuzzy Slide Mode Control to a 3-DOF Rehabilitation Robot

Cited by 7 publications

References 6 publications

An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation

An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation

RLSESN‐based PID adaptive control for a novel wearable rehabilitation robotic hand driven by PM‐TS actuators

Fuzzy Hybrid Force-Position Control for the Robotic Arm of an Upper Limb Rehabilitation Robot Powered by Pneumatic Muscles

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