Walking Assist Robot: A Novel Approach to Parameter Optimization of a Tracking Controller Compensating for Time Varying Friction

This work develops an intelligent walk assist robot for people with walking disabilities, where the precise tracking of the user’s movement is crucial. The time-varying friction and load changes impose significant challenges on the tracking. Although the digital acceleration controller is effective to handle them, it is difficult and time consuming to manually tune the control parameters for optimal performance. This is why the automatic parameter optimization techniques are popular in the literature. Despite this, the existing parameter tuning algorithms suffer from sub-optimal performance and low computational efficiency. In this paper, an improved genetic algorithm (IGA) is proposed, which can quickly identify the optimal parameters. Our algorithm explores a few advanced genetic mechanisms including nonlinear ranking selection, arithmetic crossover operation method with competition and selection mechanisms among several crossover offspring, and adaptive change of mutation scaling. The simulation and experimental results have shown that this novel IGA algorithm can effectively reduce the tracking error from 18mm to 0.08mm and reduces the computational complexity.