Neural adaptive control of a dynamic positioning system based on an event-triggered mechanism
This paper proposes a neural adaptive control algorithm based on an event-triggered mechanism for sol-ving the dynamic positioning control of surface vessels with model parameter uncertainties and environmental dis-turbances.First,an adaptive item is designed to compensate for the environmental disturbances and model parame-ter uncertainties by using the radial basis function neural network and the minimum learning parameter algorithm.The designed adaptive item has only three online learning parameters,thereby reducing the number of learning pa-rameters of the traditional neural network adaptive methods.A dynamic positioning controller is designed by combi-ning the dynamic surface control technology and an event-triggering mechanism,wherein the latter is introduced to reduce the load of information communication from the controller to the actuator and concurrently lower the execu-tion rate of the actuators.Then,the stability of the closed-loop system is analyzed using the Lyapunov theory.Finally,the effectiveness of the proposed control law is verified through simulation and comparative analysis.
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