PSO based modeling of Takagi-Sugeno fuzzy motion controller for dynamic object tracking with mobile platform

Modeling of optimized motion controller is one of the interesting problems in the context of behavior based mobile robotics. Behavior based mobile robots should have an ideal controller to generate perfect action. In this paper, a nonlinear identification Takagi-Sugeno fuzzy motion controller has been designed to track the positions of a moving object with the mobile platform. The parameters of the controller are optimized with Particle swarm optimization (PSO) and stochastic approximation method. A gray predictor has also been developed to predict the position of the object when object is beyond the view field of the robot. The combined model has been tested on a Pioneer robot which tracks a triangular red box using a CCD camera and a laser sensor.