Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger

Moshaii, Alireza Abbasi; Moghaddam, Majid M.; Niestanak, Vahid Dehghan

doi:10.1108/ir-05-2019-0110

Cited by 22 publications

(11 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The robot provides the possibility of rehabilitating each phalanx individually which is very important in the finger rehabilitation process. Moreover, due to the model uncertainties, disturbances and chattering in the system, a fuzzy sliding mode controller design method is proposed for the robot (Alireza et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Research on fuzzy impedance control of upper-limb rehabilitation robot based on membership functions online optimization learning strategy

Wang,

Dong

2023

View full text Add to dashboard Cite

Purpose To improve the position tracking efficiency of the upper-limb rehabilitation robot for stroke hemiplegia patients, the optimization Learning rate of the membership function based on the fuzzy impedance controller of the rehabilitation robot is propose. Design/methodology/approach First, the impaired limb’s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using weighted least squares method based on recursive algorithm. Second, the fuzzy impedance control with the rule has been designed with the optimal impedance parameters. Finally, the membership function learning rate online optimization strategy based on Takagi-Sugeno (TS) fuzzy impedance model was proposed to improve the position tracking speed of fuzzy impedance control. Findings This method provides a solution for improving the membership function learning rate of the fuzzy impedance controller of the upper limb rehabilitation robot. Compared with traditional TS fuzzy impedance controller in position control, the improved TS fuzzy impedance controller has reduced the overshoot stability time by 0.025 s, and the position error caused by simulating the thrust interference of the impaired limb has been reduced by 8.4%. This fact is verified by simulation and test. Originality/value The TS fuzzy impedance controller based on membership function online optimization learning strategy can effectively optimize control parameters and improve the position tracking speed of upper limb rehabilitation robots. This controller improves the auxiliary rehabilitation efficiency of the upper limb rehabilitation robot and ensures the stability of auxiliary rehabilitation training.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on fuzzy impedance control of upper-limb rehabilitation robot based on membership functions online optimization learning strategy

Wang,

Dong

2023

View full text Add to dashboard Cite

show abstract

“…The available rehabilitation systems are very large, complicated, immobile, unwieldy and very expensive. In contrast to the previously cited works [1][2][3]36,37], the proposed solution is light and portable since the objective was to design small robots with a well-defined target in order to be accessible for use at home. Compared to [14,[23][24][25][26], the developed system is a vision-based telemanipulation solution that avoids the inconveniences of the EMG-based solutions, such as signal sensitivity.…”

Section: Introductionmentioning

confidence: 99%

“…Robotics mainly focuses on the development of systems and control strategies to facilitate the recovery of lost motor skills. Systems dedicated to automated rehabilitation have benefited from numerous technological advances in robotics, such as sensors, actuators and control approaches [1][2][3][4][5][6][7]. In this context, we aimed to take advantage of the progress made in remote control systems, gestural control, IoT-based system design and fuzzy logic-based decision-support systems to design remote elbow rehabilitation solution.…”

Section: Introductionmentioning

confidence: 99%

Development of an IoT-Based Solution Incorporating Biofeedback and Fuzzy Logic Control for Elbow Rehabilitation

et al. 2020

View full text Add to dashboard Cite

The last few years have seen significant advances in neuromotor rehabilitation technologies, such as robotics and virtual reality. Rehabilitation robotics primarily focuses on devices, control strategies, scenarios and protocols aimed at recovering sensory, motor and cognitive impairments often experienced by stroke victims. Remote rehabilitation can be adopted to relieve stress in healthcare facilities by limiting the movement of patients to clinics, mainly in the current COVID-19 pandemic. In this context, we have developed a remote controlled intelligent robot for elbow rehabilitation. The proposed system offers real-time monitoring and ultimately provides an electronic health record (EHR). Rehabilitation is an area of medical practice that treats patients with pain. However, this pain can prevent a person from positively interacting with therapy. To cope with this matter, the proposed solution incorporates a cascading fuzzy decision system to estimate patient pain. Indeed, as a safety measure, when the pain exceeds a certain threshold, the robot must stop the action even if the desired angle has not yet been reached. A fusion of sensors incorporating an electromyography (EMG) signal, feedback from the current sensor and feedback from the position encoder provides the fuzzy controller with the data needed to estimate pain. This measured pain is fed back into the control loop and processed to generate safe robot actions. The main contribution was to integrate vision-based gesture control, a cascade fuzzy logic-based decision system and IoT (Internet of Things) to help therapists remotely take care of patients efficiently and reliably. Tests carried out on three different subjects showed encouraging results.

show abstract

“…In the model-free control strategies, proportional-integral-differential (PID) control has serious hysteresis that is unbeneficial to the stability of the system (Gao, 2006a). Adaptive control (Na et al , 2013), neural networks (NNS) (Fukuda and Shibata, 1992), fuzzy systems (Lee et al , 2011) and their combination (Abbasimoshaei et al , 2020; Abbasi Moshaii et al , 2019) have heavy computational burden; hence, they are not suitable for real-time control (Na et al , 2013; Wang et al , 2019; Lin and Wai, 2001). To reduce the response time and the computational burden, Extended-State-Observer (ESO) was first proposed as the core of active disturbance rejection control (ADRC) (Han, 2009).…”

Section: Introductionmentioning

confidence: 99%

Linear-extended-state-observer-based prescribed performance control for trajectory tracking of a robotic manipulator

Zhang

et al. 2021

View full text Add to dashboard Cite

Purpose Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed. Design/methodology/approach A prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory. Findings Comparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control. Originality/value In the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability.

show abstract

Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger

Cited by 22 publications

References 39 publications

Research on fuzzy impedance control of upper-limb rehabilitation robot based on membership functions online optimization learning strategy

Research on fuzzy impedance control of upper-limb rehabilitation robot based on membership functions online optimization learning strategy

Development of an IoT-Based Solution Incorporating Biofeedback and Fuzzy Logic Control for Elbow Rehabilitation

Linear-extended-state-observer-based prescribed performance control for trajectory tracking of a robotic manipulator

Contact Info

Product

Resources

About