Abstract
A novel interval type-2 intuition fuzzy brain emotional learning network model (IT2IFBELC) which depends only on the input and output data is proposed for the rehabilitation robot, which is different from model-based control algorithms that require exact dynamic model knowledge of the rehabilitation robot. The proposed model takes advantage of the type-2 intuition fuzzy theory and brain emotional neural network, and this is no rule initially; then, the structure and parameters of IT2IFBELC are tuned online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through an interval type-2 intuition fuzzy inference system (IT2IFIS), and then the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the whole controller is composed of three parts, including the ideal sliding mode controller, the interval type-2 intuition fuzzy brain emotional learning network controller, and a powerful robust compensation controller, and then one Lyapunov function is designed to guarantee the rapid convergence of the control systems. For further illustrating the superiority of this model, several models are studied here for comparison, and the results show that the interval type-2 intuition fuzzy brain emotional learning network model can obtain better satisfactory control performance and be suitable to deal with the influence of the uncertainty of the rehabilitation robot.
Highlights
Rehabilitation robots can significantly improve the motor ability and quality of life of people with reduced limb function
There are several products that can meet the requirements of rehabilitation robot control, the coupling performance of the human rehabilitation robot used to help the elderly and disabled is still very insufficient
One therapist rehabilitation robot in this paper is designed to be worn to provide rehabilitation therapy for the stroke patients [1,2,3,4]. e effective control strategies are so important for the rehabilitation robot to operate coordinately with the human upper limb
Summary
Rehabilitation robots can significantly improve the motor ability and quality of life of people with reduced limb function. E main contributions of this paper include the following: (1) the novel interval type-2 intuition brain emotional learning network model is proposed firstly; (2) the parameters can be tuned online by adaptive laws; (3) the structure of the interval type-2 intuition brain emotional learning network can be constructed automatically from the empty initial rule; (4) the stability of this proposed control system is guaranteed by Lyapunov function; and (5) numerical simulations have been made to demonstrate the effectiveness of the proposed method for the multiple degree-of-freedom rehabilitation robot.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
