MBPC controller for UGV Trajectory Tracking

In this paper, the development of a tracking trajectories system for a skid-steering type agricultural vehicle based on predictive control is presented. Since the vehicle operates outdoors, a high precision measurement of the geolocation and orientation is needed, therefore, a Kalman filter was designed. That filter fusions the data of position and orientation from an inertial measurement unit (IMU), wheel encoders and GPS (Global Positioning System) mounted on the vehicle. On the other hand, for the autonomous tracking trajectories, two controllers were designed: first, a proportional controller for the linear speed, and second, a model based predictive controller (MBPC) for the vehicle heading angle. For the design of the MBPC, a dynamical model of the vehicle direction was obtained using a gray box modeling, additionally, an incremental state space model strategy was use to design and deploy the MBPC. Finally, the results show that the developed control strategy presents a good performance with a RMSE of 1.038m in tracking outdoor trajectories.