In this paper a terminal sliding-mode adaptive control scheme for robotic manipulators designed following an energybased approach is presented. The control comprises two basic terms: a composite adaptive term which implements a feedback law for compensating the modelled dynamics and a non-linear sliding-mode term for overcoming the unmodelled dynamics and perturbations. The resulting closed-loop system is proved to be stable and it is also shown that the trajectory-tracking error converges to zero in finite time. Moreover, an upper bound of this error convergence time is calculated. Finally, the design is evaluated by means of simulations.

A neural network controller which is used for controlling unknown discrete-time DARMA systems is described. A two-layered neural network is used to estimate the unknown plant dynamics. The well known Widrow-Hoff delta rule is used as the learning algorithm for this network, to minimise the difference between the plant actual response and that predicted by the neural network. The control law is generated online using a second two-layered neural network, so that the plant output is brought to a desired reference signal. It is proved that the control objective is achieved by the closed-loop system and that the system remains closed-loop stable. Some simulation examples are also presented to evaluate the design.

This paper presents a robust control scheme for flexible link robotic manipulators, which is based on considering the flexible mechanical structure as a system with slow (rigid) and fast (flexible) modes that can be controlled separately. The rigid dynamics is controlled by means of a robust sliding-mode approach with well-established stability properties while an LQR optimal design is adopted for the flexible dynamics. Experimental results show that this composite approach achieves good closed loop tracking properties both for the rigid and the flexible dynamics.