Control of a New Cycling Rehabilitation Robot Based on Fuzzy PID

Jiang, Dawei; Shi, Guozhen; Pang, Zaixiang; Li, Shuang; Tian, Yuan

doi:10.1088/1742-6596/1622/1/012119

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…It is almost impossible to formulate an accurate mathematical model for nonlinear, time-varying, human-robot interacting systems [12][13][14]. In order to achieve better performance of controller, a lot of advanced control strategies have been employed to the passive control of lower limb rehabilitation robots including fuzzy control [15], sliding mode control [16], adaptive controller [17], neural network control [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Adaptive sliding mode control strategy based on disturbance observer and neural network for lower limb rehabilitative robot

Wang

et al. 2022

IET Control Theory & Appl

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In this study, to achieve accurate tracking of the desired trajectory during passive control of the lower limb rehabilitation robot, an adaptive sliding mode controller based on disturbance observer and radial basis function neural network (RBFNN) is proposed for the lower limb rehabilitative robot in the presence of uncertain parameters and external bounded disturbances. First, the Euler–Lagrange dynamic model of the lower limb rehabilitative robot is described. Second, a sliding mode controller is designed to stabilize the system with an improved sliding mode reach rate under the assumption that all parameters of the dynamics model are known. To achieve a sliding mode controller without the above assumptions, the proposed adaptive RBFNN and the disturbance observers are employed to compensate for disturbances and the uncertainties in the robot's dynamic mode via feedforward loops. The Lyapunov stability theory is used to prove that the proposed controller has accomplished a significant control effect with excellent performance and the output tracking error can be converted to a very small neighborhood through reasonable design parameters. Finally, the performance of the controller based on the state feedback and state observer are demonstrated by numerical simulations, respectively.

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Section: Introductionmentioning

confidence: 99%

Adaptive sliding mode control strategy based on disturbance observer and neural network for lower limb rehabilitative robot

Wang

et al. 2022

IET Control Theory & Appl

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“…Setting up the dynamic equation for each link is an essential factor to calculate and simulate the motion of the joints, from which it is possible to calculate the design of the mechanical structure and the controller so that it is suitable for research models. To establish the dynamic equation of the device [13][14], we conduct a dynamic analysis of the joints. The diagram of the dynamic analysis of the research model is shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Response of the Rehabilitation Lower Device using Sliding Mode Controller

Duc

Tuy

Phuoc

2021

Eng. Technol. Appl. Sci. Res.

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This paper presents the application of the sliding mode controller to simulate the control of a 3 Degree of Freedom (DoF) robot model that supports lower extremity rehabilitation exercises for stroke patients. The 3 DoF robot model has an established dynamic differential equation for each joint. This paper studied the response of links of the research model with a sliding mode controller proposed as the control solution. Matlab software was used in simulations of ankle, knee, and hip joints' drive for 2 cases, without and with load. Compared with other studies, the research results show that the sliding controller results in slight angular errors when applied to the research model, while the torque of the joints remains low. This result is the basis for calculating and selecting parameters for the actuator when manufacturing the actual equipment.

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A Graphical User Interface (GUI) System for a Stationary Trainer Used in Lower Limb Rehabilitation

Sunilkumar

Mohan

Rybak

2022

Lecture Notes in Mechanical Engineering

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Control of a New Cycling Rehabilitation Robot Based on Fuzzy PID

Cited by 3 publications

References 11 publications

Adaptive sliding mode control strategy based on disturbance observer and neural network for lower limb rehabilitative robot

Adaptive sliding mode control strategy based on disturbance observer and neural network for lower limb rehabilitative robot

A Study on the Response of the Rehabilitation Lower Device using Sliding Mode Controller

A Graphical User Interface (GUI) System for a Stationary Trainer Used in Lower Limb Rehabilitation

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