Research on Fuzzy Adaptive Interactive Control of Upper Limb Rehabilitation Robots

doi:10.12068/j.issn.1005-3026.2024.07.009

Abstract

Abstract:

An adaptive interactive control system based on fuzzy rules for upper limb rehabilitation robots is proposed to address the insufficient or excessive training intensity during active rehabilitation exercises for stroke patients due to their individual differences. According to the difference of muscle strength of patients with different conditions， a fuzzy adaptive impedance controller is designed， which adjusts the damping and stiffness coefficients adaptively with fuzzy inference based on human?machine interaction forces and system errors， altering the training intensity to achieve on?demand assistive control for rehabilitation robots. Additionally， to ensure accurate tracking of the motion trajectory during rehabilitation training， a GA-FuzzyPID controller is designed to optimize the fuzzy rule membership functions and rule base according to an improved genetic algorithm， thereby reducing the trajectory tracking error of rehabilitation robots. Finally， trajectory tracking and adaptive impedance controlling simulation experiments are conducted for the system based on Matlab/Simulink. The results show that in the trajectory tracking experiment， the trajectory error of GA-FuzzyPID controller is reduced by 55.9% and 34.0% respectively compared with PID controller and FuzzyPID controller， which effectively improves the trajectory tracking accuracy. Compared with the fixed impedance method， the adaptive impedance control experiment verifies the effectiveness and feasibility of the proposed adaptive interactive control system.

Key words: upper limb rehabilitation robot, impedance control, fuzzy rule, genetic algorithm, adaptive control

CLC Number:

TP 242

Quan SHAN, Shun ZHANG, Jian-cong HUANG, Yan CHEN. Research on Fuzzy Adaptive Interactive Control of Upper Limb Rehabilitation Robots[J]. Journal of Northeastern University(Natural Science), 2024, 45(7): 974-983.

Figures/Tables 25

Fig.1 Schematic diagram of shoulder and elbow joint movement

Table1 Range of shoulder and elbow jointmotion

关节	运动类型	角度/(°)
肩关节	屈曲	0~180
	伸展	0~50
	外展	0~60
	内收	0~45
肘关节	旋外	0~80
	旋内	0~100
	屈曲	0~145
	伸展	0~5

Fig. 2 Upper limb rehabilitation robot model

Fig.3 Upper limb rehabilitation robot prototype and human?machine interaction demonstration

Fig.4 Schematic diagram of the upper limb rehabilitation robot

Fig.5 Schematic diagram of the fuzzy adaptive interactive control system

Fig.6 Fuzzy rule schematic

Table2 Fuzzy rule table of stiffness parameters

e	F_e
e	NL	NS	ZE	PS	PL
NL	NL	NL	NS	ZE	ZE
NS	NL-	NS	NS	ZE	PS
ZE	NS	NS	ZE	PS	PS
PS	NS	ZE	PS	PS	PL
PL	ZE	ZE	PS	PL	PL

Table3 Fuzzy rule table of damping parameters

e′	F_e
e′	NL	NS	ZE	PS	PL
NL	NL	NS	NS	ZE	PS
NS	NL	NS	NS	ZE	PS
ZE	NS	ZE	ZE	PS	PL
PS	NS	ZE	PS	PS	PL
PL	NS	PS	PS	PL	PL

Fig.7 Flowchart of fuzzy rule optimization by genetic algorithm

Fig.8 Fuzzy rules

Fig.9 Simulation model of the adaptive interactive control system

Fig.10 Optimal membership curves

Table 4 kp fuzzy rule base

ec	e
ec	NL	NS	ZE	PS	PL
NL	ZE	PL	PL	ZE	NL
NS	PS	NL	ZE	PL	PL
ZE	PS	PS	NS	ZE	PS
PS	NL	NS	ZE	PS	PS
PL	NL	NS	ZE	ZE	ZE

Table 5 ki fuzzy rule base

ec	e
ec	NL	NS	ZE	PS	PL
NL	NS	PS	NS	ZE	PL
NS	NL	ZE	NL	PS	PL
ZE	ZE	PS	ZE	NS	NL
PS	PS	PS	PS	NS	PL
PL	PS	PL	PL	PL	PS

Table6 kd fuzzy rule base

ec	e
ec	NL	NS	ZE	PS	PL
NL	NS	PS	PL	ZE	PL
NS	NS	NS	PS	NS	PL
ZE	NL	ZE	PS	PS	NS
PS	PS	PS	ZE	ZE	ZE
PL	ZE	PL	PS	ZE	NS

Fig.11 Iterative comparison of genetic algorithms

Fig.12 Trajectory tracking curve

Fig.13 Trajectory tracking error

Table7 Controller performance indicators

控制器	性能指标
控制器	max( $e$ )	ISE	ITSE	ITAE
PID	0.752	3.343	20.24	34.37
FuzzyPID	0.503	1.568	8.982	22.89
GA-FuzzyPID	0.332	0.685	3.991	15.26

Table7 Controller performance indicators

控制器	性能指标
控制器	max( $e$ )	ISE	ITSE	ITAE
PID	0.752	3.343	20.24	34.37
FuzzyPID	0.503	1.568	8.982	22.89
GA-FuzzyPID	0.332	0.685	3.991	15.26

Fig.14 Human?machine interaction force

Fig.15 Comparison of impedance trajectory tracking of Group A

Fig.16 Comparison of impedance parameters of Group A

Fig.17 Comparison of impedance trajectory tracking of Group B

Fig.18 Comparison of impedance parameters ofGroup B

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