Compound learning based event-triggered adaptive attitude control for underwater gliders with actuator saturation and faults

This paper proposes an event-triggered compound learning adaptive attitude control scheme for underwater gliders (UGs) with unknown dynamics, actuator saturation, and actuator faults. To begin with, in the framework of adaptive dynamic surface control (DSC), a historical input–output data based neural network (NN) is employed to estimate and compensate for unknown dynamics, external disturbance and actuator faults as a whole. For the merit of the composite learning and integral concurrent learning techniques, the NN weights are updated in a compound learning fashion where instant tracking errors, prediction errors, and historical estimation errors are combined in the adaptive law, making the NN weights converge to their true value with a good transient performance. Furthermore, a hyperbolic tangent function is utilized to explicitly handle the input saturation constraint, and the singular problem is circumvented by a Nussbaum gain function. Finally, a novel event-triggering condition is designed to save communication resource and reduce energy consumption. Theoretical proofs are provided to ensure the semi-global uniformly ultimately bounded (SGUUB) stability of the closed-loop system. The effectiveness of the proposed scheme is verified by simulation studies under various conditions.