The above-knee intelligent bionic leg is very helpful to amputees in the area of rehabilitation medicine. This paper first introduces the functional demand of the above-knee prosthesis design. Then, the advantages of the four-bar link mechanism and the magneto-rheological (MR) damper are analyzed in detail. The fixed position of the MR damper is optimized and a virtual prototype of knee joint is given. In the end, the system model of kinematics, dynamics, and controller are given and a control experiment is performed. The control experiment indicates that the intelligent bionic leg with multi-axis knee is able to realize gait tracking of the amputee s healthy leg based on semi-active control of the MR damper.
To enable exoskeleton wearers to walk on level ground, estimation of lower limb movement is particularly indispensable. In fact, it allows the exoskeleton to follow the human movement in real time. In this paper, the general regression neural network optimized by golden section algorithm (GS-GRNN) is used to realize prediction of the human lower limb joint angle. The human body hip joint angle and the surface electromyographic (sEMG) signals of the thigh muscles are taken as the inputs of a neural network to predict joint angles of lower limbs. To improve the prediction accuracy in different gait phases, the plantar pressure signals are also added into the input. After that, the error between the prediction result and the actual data decreases significantly. Finally, compared with the prediction result of the BP neural network, GRNN shows splendid prediction performance for its less processing time and higher prediction accuracy.
A visual servoing tracking controller is proposed based on the sliding mode control theory in order to achieve strong robustness against parameter variations and external disturbances. A sliding plane with time delay compensation is presented by the pre-estimate of states. To reduce the chattering of the sliding mode controller, a modified exponential reaching law and hyperbolic tangent function are applied to the design of visual controller and robot joint controller. Simulation results show that the visual servoing control scheme is robust and has good tracking performance.
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