چكيده به لاتين
Regarding wide-range applications of robots in different areas such as science, industry and public services, developing optimized control algorithms in this domain has drawn considerable attention over past few years.In this thesis a method for control of Wheeled Mobile Robot (WMR) is presented. Dynamic and Kinematic model of WMR is obtained. In this method parameters in dynamic model is unknown. By employing adaptive Neural Networks and a barrier Lyapunov function with error variables, the dynamic model of mobile robot are estimated, and the physical constraints are not violated. The constraints come from the limitations of the wheels’ forward speed and steering angular velocity, which depends on the motors’ driving performance. Also, a fuzzy system is designed to avoid obstacles. The proposed scheme can guarantee the uniform ultimate boundedness for all signals in the WMR system, and the tracking error converge to a bounded compact set to zero. Thus, using adaptive Neural Networks and a barrier Lyapunov function as dynamic controller and fuzzy system as kinematic controller is the method for controlling differential mobile robot in presence of obstacles in this project. For examine the performance of presented method, a WMR setup is developed and ROS is used for operating system in WMR. Also, for comparison with presented dynamic controller, two other dynamic controller is employd on WMR. Effectiveness of the proposed scheme is illustrated by Experimental results on a WMR system.