The application of fuzzy Kohonen clustering network for intelligent wheelchair motion control

Abstract

According to the two-dimensional coordinate of users body gravity center, a motion control method was proposed for the omni-directional intelligent wheelchair. Fuzzy Kohonen clustering neural network was used in the clustering process of human gravity center in order to judge the occupants driving direction intention. To reduce the effect caused by abnormal clustering center data points which were generated by malfunction, the improvements on fuzzy membership function and learning rate of fuzzy Kohonen clustering neural network were proposed in this paper. At the same time, this motion control method of omni-directional intelligent wheelchair was combined with similarity theory, which was focus on eliminating the isolated points generated by malfunction in the application phase. Comparative experiments confirmed that the improved fuzzy Kohonen clustering neural network algorithm was with lower error rate than the traditional one. Physical experiments proved that this algorithm could remove the isolated gravity center points that were generated by malfunction, and it could reduce the error between the desired moving directions and the actual moving directions. The simulation results further demonstrated the practical feasibility and validity of the proposed control algorithm, and the motion control precision was also improved for omni-directional intelligent wheelchair.