Nussbaum-type function based adaptive fuzzy event-triggered control for dynamic positioning vehicles under input dead-zone

The dynamic positioning (DP) control task aims to stabilize the DP vehicle at a certain coordinate point while maintaining the appointed attitude. To improve the control performance of DP vehicle, this article focuses on solving input dead-zone and actuator wear problems. In practice, both the actuator gains and dead-zone may cause unknown input characters, while the existing DP control solutions paid less attention to the latter. To fix this issue, a nonlinear symmetric dead-zone model that depends on the input signals is defined. Moreover, the unknown system dynamics under the dead-zone constraints is approximated by the fuzzy logic system for the sake of control accuracy. In the meantime, a fuzzy logic state observer is designed to estimate the unmeasured states of DP vehicle. To reduce the actuator wear and extend the control period, an adjustable threshold event-triggered strategy is designed. Compared with the existing event-triggered results, the proposed one can achieve balance between control accuracy and triggered performance. The stability of the control system is demonstrated by applying the Lyapunov theory. Ultimately, the validity of the algorithm in this paper is demonstrated through experiments based on the Matlab platform.