  This paper presents a Tracking-Error Learning Control (TELC) algorithm for
precise mobile robot path tracking in off-road terrain. In traditional tracking
error-based control approaches, feedback and feedforward controllers are
designed based on the nominal model which cannot capture the uncertainties,
disturbances and changing working conditions so that they cannot ensure precise
path tracking performance in the outdoor environment. In TELC algorithm, the
feedforward control actions are updated by using the tracking error dynamics
and the plant-model mismatch problem is thus discarded. Therefore, the
feedforward controller gradually eliminates the feedback controller from the
control of the system once the mobile robot has been on-track. In addition to
the proof of the stability, it is proven that the cost functions do not have
local minima so that the coefficients in TELC algorithm guarantee that the
global minimum is reached. The experimental results show that the TELC
algorithm results in better path tracking performance than the traditional
tracking error-based control method. The mobile robot controlled by TELC
algorithm can track a target path precisely with less than $10$ cm error in
off-road terrain.
