Study on the control algorithm for lower limb exoskeleton based on ADAMS/Simulink co-simulation

In this article a study of algorithms for human movement in the lower limbs exoskeleton is presented. Human-machine system is considered, the classification of the existing exoskeletons by type of power distribution, the features of stable motion of the mechanism are presented. The law of the necessary trajectory of the center of mass of the exoskeleton is shown in the sagittal and frontal planes to maintain stability. The synchronous motion scheme of the centre of mass and the foot is described.

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Geriatric Walk Assist Robot - Design, Analysis and Implementation of a Modular Lower Limb Exoskeleton Robot

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.852.770 ◽

2016 ◽

Vol 852 ◽

pp. 770-775 ◽

Cited By ~ 1

Author(s):

P. Karthikeyan ◽

Gopal Satheesh Kumar ◽

M. Ajin

Keyword(s):

Lower Limb ◽

Control Algorithm ◽

Applied Pressure ◽

Pressure Sensors ◽

Gait Pattern ◽

The Body ◽

Sensors And Actuators ◽

Aged People ◽

Lower Limb Exoskeleton ◽

Exoskeleton Robot

Among the problems of major concern faced by the geriatric community the fore most is considered to be: “as we grow older it gets difficult to walk”. As they lose their strength to withstand their weight they become weak to walk on their own. Through this project a design is proposed for an assistive modular lower limb exoskeleton robot to enable aged people to walk on their own. The design is mainly based on the amplification of the pressure applied on the thighs and ankles of the legs and these pressures are used to move the legs of the robot which support the legs of the user. Since the user is capable of generating a minimum pressure on their own the working of the gait pattern is mainly based on the movement of knee and ankle, and so only the knee and the ankle are considered in this design. Another reason is that the major weight of the body acts on the knee and the foot while walking. The working of the robot is mainly based on the layered control algorithm embedded on the microcontroller acting through the sensors and actuators. Pressure sensors are used to measure the applied pressure and electrical actuators are used due to their lesser weight. Based on the experimental results obtained with the working robot the design would be fine-tuned for optimized performance.

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Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

Complexity ◽

10.1155/2021/6651495 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Wei Guan ◽

Lan Zhou ◽

YouShen Cao

Keyword(s):

Motion Control ◽

Lower Limb ◽

Iterative Learning Control ◽

Control Algorithm ◽

Learning Control ◽

Iterative Learning ◽

Knee Joints ◽

Lower Limb Exoskeleton ◽

Exoskeleton Robot

At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.

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