Abstract
This paper develops a depth information robust adaptive control algorithm with event triggered input for underactuated surface vessels with dynamic uncertainty and limited communication resources. The controller uses radial basis function (RBF) neural networks to approximate the model uncertainty. This paper designs an event triggered input because of the limited communication resources. The stability of the depth information robust adaptive control is rigorously proved via Lyapunov analysis. In comparing with the finite time control scheme without event-triggered and adaptive ANNs control scheme with minimum learning parameters (MLPs) and artificial neural network (ANNs), the event-triggered method can obtain better control effect.
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