  This letter presents a new intelligent control scheme for the accurate
trajectory tracking of flexible link manipulators. The proposed approach is
mainly based on a sliding mode controller for underactuated systems with an
embedded artificial neural network to deal with modeling inaccuracies. The
adopted neural network only needs a single input and one hidden layer, which
drastically reduces the computational complexity of the control law and allows
its implementation in low-power microcontrollers. Online learning, rather than
supervised offline training, is chosen to allow the weights of the neural
network to be adjusted in real time during the tracking. Therefore, the
resulting controller is able to cope with the underactuating issues and to
adapt itself by learning from experience, which grants the capacity to deal
with plant dynamics properly. The boundedness and convergence properties of the
tracking error are proved by evoking Barbalat's lemma in a Lyapunov-like
stability analysis. Experimental results obtained with a small single-link
flexible manipulator show the efficacy of the proposed control scheme, even in
the presence of a high level of uncertainty and noisy signals.
