Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot

Abstract

Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the patient’s interactive force situation, resulting in the inability to accurately assess the patient’s true motor intention and difficulty stimulating the patient’s initiative, thus negatively affecting the patient’s rehabilitation outcome. Therefore, this paper proposes a fuzzy adaptive passive (FAP) control strategy based on subjects’ task performance and impulse. To ensure the safety of subjects, a passive controller based on the potential field is designed to guide and assist patients in their movements, and the stability of the controller is demonstrated in a passive formalism. Then, using the subject’s task performance and impulse as evaluation indicators, fuzzy logic rules were designed and used as an evaluation algorithm to quantitively assess the subject’s motor ability and to adaptively modify the stiffness coefficient of the potential field and thus change the magnitude of the assistance force to stimulate the subject’s initiative. Through experiments, this control strategy has been shown to not only improve the subject’s initiative during the training process and ensure their safety during training but also enhance the subject’s motor learning ability.

Incubators in Neonatal Medicine

“…Another papers present the application of adaptive fuzzy sliding mode control (AFSMC) for a respiratory system to assist the patients facing difficulty in breathing [83]. Compared to neonatal care, this adaptive sliding mode control (AFSMC) could play an important role in controlling parameters of newborn in an incubator [83,84].…”

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

“…Defuzzification converts the output values of the fuzzy set into crisp values by using some linguistic rules of if-then and logical operators [ 23 ]; in this design, we will use the AND operator. There are many equations to calculate defuzzification, and the centroid of area (CoA) is used in this design.…”

“…where X is the new input that requires decision making; and a, m, and b are the points to determine the interval of each triangle in the membership function, with µ A (X) as the output of Equation ( 1). Defuzzification converts the output values of the fuzzy set into crisp values by using some linguistic rules of if-then and logical operators [23]; in this design, we will use the AND operator. There are many equations to calculate defuzzification, and the centroid of area (CoA) is used in this design.…”