  This paper develops a Closed-Loop Error Learning Control (CLELC) algorithm
for feedback linearizable systems with experimental validation on a mobile
robot. Traditional feedback and feedforward controllers are designed based on
the nominal model by using Feedback Linearization Control (FLC) method. Then,
an intelligent controller is designed based on sliding mode learning algorithm
that utilizes closed-loop error dynamics to learn the system behavior. The
controllers are working in parallel, and the intelligent controller can
gradually replace the feedback controller from the control of the system. In
addition to the stability of the sliding mode learning algorithm, the
closed-loop stability of an $n$th order feedback linearizable system is proven.
The simulation results demonstrate that CLELC algorithm can improve control
performance (e.g., smaller rise time, settling time and overshoot) in the
absence of uncertainties, and also provides robust control performance in the
presence of uncertainties as compared to traditional FLC method. To test the
efficiency and efficacy of CLELC algorithm, the trajectory tracking problem of
a tracked mobile robot is studied in real-time. The experimental results
demonstrate that CLELC algorithm ensures high-accurate trajectory tracking
performance than traditional FLC method.
