The Application of Model Predictive Control (MPC) to Fast Systems such as Autonomous Ground Vehicles (AGV)

This paper investigates the application of Model Predictive Control (MPC) to fast systems such as Autonomous Ground Vehicles (AGV) or mobile robots. The control of Autonomous ground vehicles (AGV) is challenging because of nonholonomic constraints, uncertainties, speed, accuracy of controls and the vehicle's terrain of operation. Two nonlinear models: a car-like model and a bicycle model are considered. A Nonlinear MPC (NMPC) was developed. A trajectory tracking performance index for both models was studied. After thorough and extensive simulation, it is observed that both models are applicable in the context of NMPC and the constraints on model variables were adequately respected. The trajectories were successfully tracked and thus clearly indicate the efficiency and effectiveness of the MPC technique. In order to improve on speed and reduce the computational effort required for the optimization problem, a Linear MPC (LMPC) was implemented with both models. This is possible by successive linearization along the reference trajectory and formulating a quadratic optimization problem which is solved by implementing an interior-point quadratic programming algorithm. For both AGV models, analysis concerning the reduced computational efforts is presented in order to show the viability of LMPC technique developed.