A MULTI-PROCESSOR ARCHITECTURE FOR THE REAL-TIME SWITCHING CONTROL OF MOBILE ROBOTS

摘要

The paper describes a real-time multi-processor architecture to control a mobile robot in a real context with environment disturbances and parametric uncertainties. The proposed methodology utilizes multiple models of the robot for its identification in an adaptive and learning control framework. Radial Basis Function Networks (RBFN) are considered for the multiple models. The experimental results are satisfactory in terms of tracking errors and computational efforts and show the improvement in the tracking performance when the proposed control architecture is used for tracking tasks in dynamical uncertain environments.