Abstract
In this paper, a novel event-triggered composite learning finite-time control scheme is presented for underactuated marine surface vehicles (MSVs) trajectory tracking under unknown dynamics and unknown time-varying disturbances. Line-of-sight (LOS) tracking control method is employed to address the underactuation problem of MSVs. The neural networks (NNs) are untilized to approximate unknown dynamics. The serial-parallel estimation model is employed to construct the prediction error, and the prediction errors and tracking errors are fused with construct the NN weights updating. Combining the result of approximation information, the disturbances observers can be created to achieve disturbance estimation. Fractional power technology is artistically introduced to realize the finite-time trajectory tracking control of MSVs based on composite learning. The proposed control scheme ensures the simultaneous realization of high precision tracking performance and unknown information approximation. Moreover, an event-triggered mechanism is introduced to reduce the transmission load and the execution rate of actuators. It is proved that the proposed control scheme ensures all error signals of the MSVs trajectory tracking control system can converge to the neighborhood of zero within a finite time. Finally, the simulation results on an MSV verify the effectiveness and superiority of the proposed control scheme.
Highlights
Marine surface vehicles (MSVs) have made significant contributions to the growth of the marine economy in recent years
Motivated by the review on previous works, an eventtriggered composite learning finite-time control scheme for the trajectory tracking control of MSVs under time-varying disturbances and unknown dynamics is presented.This is the first attempt to propose an event-triggered finite time control scheme based on composite learning, while ensures the highprecision tracking control of the MSVs and the execution rate of the actuator are drastically reduced
The proposed control scheme in this paper is marked as τetc−cl
Summary
Marine surface vehicles (MSVs) have made significant contributions to the growth of the marine economy in recent years. In [42], an robust adaptive finite-time tracking control scheme was proposed by using non-smooth and homogeneous tools for MSVs with unknown disturbances. Motivated by the review on previous works, an eventtriggered composite learning finite-time control scheme for the trajectory tracking control of MSVs under time-varying disturbances and unknown dynamics is presented.This is the first attempt to propose an event-triggered finite time control scheme based on composite learning, while ensures the highprecision tracking control of the MSVs and the execution rate of the actuator are drastically reduced. Compared with the prediction error construction method in [28], [29], the fractional power technology is artistically introduced into the SPEM to design the prediction error, which realizes the finite-time control under the composite learning scheme.
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