Model predictive control and trajectory optimization of large vehicle-manipulators

In this paper, a model predictive control (MPC) is developed for on-and off-road mid-sized heavy duty vehicle-manipulator systems with a hydraulic working arm. The proposed concept for the control model is also new in the sense of working only within a local reference coordinate-system relative to the reference trajectory (so-called Frenet-System). The control model only needs the errors to the reference trajectory. In contrast to other state-of-the-art approaches, there is no global localization method necessary. The control model is kept as simple as possible, to allow real-time motion prediction of the real system. For this reason, a kinematic model is used in the MPC which consists of a bicycle model and a planar robotic arm with two control variables. The dynamics of the overall system are considered as optimization constraints, assuming that the optimized system inputs and states are kinetically and dynamically feasible. Through this control method, the dual-trajectories are also optimized and they provide smooth motions for the overall system. The underlying control of the robotic arm is realized with a proportional-integral-derivative (PID) controller with feedback linearisation and gravity compensation. The control algorithm is tested and validated in a MATLAB/Simulink simulation environment.