Mathematical modelling and model predictive control of the unmanned ground vehicle

Unmanned ground vehicles are becoming very useful in modern days. They can perform many tasks autonomously and remotely. For better performance and efficiency of UGV, a better control system is needed. In this thesis, kinematic and dynamic models of unmanned ground vehicle are developed to accurately describe the dynamics of the system in trajectory tracking. Simulink block diagrams of these models are built to study the behavior of the system by simulation. Trajectory planning in an environment with obstacles using probabilistic roadmap (PRM) and a cubic polynomial equation is done to avoid obstacles while reaching the goal. A model predictive control (MPC) based control system is presented as an alternative approach to minimize the position error. Simulation results for MPC of the UGV in MATLAB Simulink are presented which demonstrate that the MPC based control system can minimize the position error