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