Abstract
A new model-free adaptive robust control method has been proposed for the robotic exoskeleton, and the proposed control scheme depends only on the input and output data, which is different from model-based control algorithms that require exact dynamic model knowledge of the robotic exoskeleton. The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.
Highlights
Exoskeleton robots can signi cantly improve the motorE ability and quality of life of people with reduced limb function
There are several products that can meet the requirements of exoskeleton robot control, the coupling performance of human exoskeleton robot used to help the the body functions of this group of people can be restored to elderly and disabled is still very insu cient
[17], model-free adaptive control was combined with neural network control, and the parameters of the controller are Radjusted online real-time through neural network, and flight control; (3) the new controller can effectively enhance the anti-interference capability of the system; (4) the algorithm proposed has the characteristics of reliability and is verified in the comparison with other two algorithms
Summary
E ability and quality of life of people with reduced limb function. With the acceleration of the aging process, the number of patients with stroke, Parkinson’s disease, spinal injury, and lower extremity paraplegia will continue to in-. Most of the control methods are designed based on the model of robot. With the development of the technology, the data-driven method now is an alternative effective way, which does not need the model of interacting. Data-driven Control (DDC) method does not contain the mathematical model information of the controlled process in designing process explicitly or implicitly, which. In literature [17], model-free adaptive control was combined with neural network control, and the parameters of the controller are Radjusted online real-time through neural network, and flight control; (3) the new controller can effectively enhance the anti-interference capability of the system; (4) the algorithm proposed has the characteristics of reliability and is verified in the comparison with other two algorithms
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