Experimental Validation of Linear and Nonlinear MPC on an Articulated Unmanned Ground Vehicle

This paper focuses on the trajectory tracking control problem for an\narticulated unmanned ground vehicle. We propose and compare two approaches in\nterms of performance and computational complexity. The first uses a nonlinear\nmathematical model derived from first principles and combines a nonlinear model\npredictive controller (NMPC) with a nonlinear moving horizon estimator (NMHE)\nto produce a control strategy. The second is based on an input-state\nlinearization (ISL) of the original model followed by linear model predictive\ncontrol (LMPC). A fast real-time iteration scheme is proposed, implemented for\nthe NMHE-NMPC framework and benchmarked against the ISL-LMPC framework, which\nis a traditional and cheap method. The experimental results for a time-based\ntrajectory show that the NMHE-NMPC framework with the proposed real-time\niteration scheme gives better trajectory tracking performance than the ISL-LMPC\nframework and the required computation time is feasible for real-time\napplications. Moreover, the ISL-LMPC produces results of a quality comparable\nto the NMHE-NMPC framework at a significantly reduced computational cost.\n