Variable admittance compliance control of upper limb training and rehabilitation robot based on genetic algorithm optimization

The insufficient motion compliance of rope-driven rehabilitation robots during rehabilitation training, combined with the inability of widely used admittance control techniques in robotics to adapt to real-time compliance adjustments for rope-driven rehabilitation robots, presents a challenge. Moreover, traditional variable admittance control exhibits poor adaptability when faced with the individual differences of different patients, making it difficult to dynamically adjust control parameters based on the patient's real-time status and needs, leading to suboptimal rehabilitation outcomes. Therefore, this article takes the independently developed rope traction upper limb rehabilitation robot as the research object, and adopts a combination of genetic algorithm (GA), fuzzy controller, and admittance control to improve training effectiveness and safety. A new rehabilitation training equipment variable admittance control strategy is proposed to meet both clinical rehabilitation and daily training needs. This strategy utilizes genetic algorithm to optimize the parameters of the fuzzy controller, so that the output of the fuzzy controller can meet the real-time adjustment of admittance characteristics according to the patient's motion state, achieving precise control of force and motion, as well as autonomous adjustment of the flexibility of the rehabilitation robot. Simulation and experimental results show that the fuzzy control variable admittance model optimized based on genetic algorithm improves the accuracy by about 3.178% compared with the traditional admittance model, and at the same time, the system can still maintain good stability under load changes. During the patient's training process, the variable admittance control model optimized by the genetic algorithm demonstrated significant performance improvements in the upper limb rehabilitation robot system. This model enables the system to reach a stable state in a shorter time while maintaining smooth and accurate motion trajectories, greatly enhancing the safety and effectiveness of the training.