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Event-triggered adaptive neural network trajectory tracking control for underactuated ships under uncertain disturbance

Su Wenxue, Zhang Qiang, Liu Yufeng

Polish Maritime Research

2023

nr 3

119--131

An adaptive neural network (NN) event-triggered trajectory tracking control scheme based on finite time convergence is proposed to address the problem of trajectory tracking control of underdriven surface ships. In this scheme, both NNs and minimum learning parameters (MLPS) are applied. The internal and external uncertainties are approximated by NNs. To reduce the computational complexity, MLPs are used in the proposed controller. An event-triggered technique is then incorporated into the control design to synthesise an adaptive NN-based event-triggered controller with finite-time convergence. Lyapunov theory is applied to prove that all signals are bounded in the tracking system of underactuated vessels, and to show that Zeno behavior can be avoided. The validity of this control scheme is determined based on simulation results, and comparisons with some alternative schemes are presented.

Finite-time integral sliding mode control for chaotic permanent magnet synchronous motor systems

Chibani A., Daaou B., Gouichiche A., Safa A., Messlem Y.

Archives of Electrical Engineering

2017

Vol. 66, nr 2

229--239

In this paper, an integral finite-time sliding mode control scheme is presented for controlling a chaotic permanent magnet synchronous motor (PMSM). The controller can stabilize the system output tracking error to zero in a finite time. Using Lyapunov’s stability theory, the stability of the proposed scheme is verified. Numerical simulation results are presented to present the effectiveness of the proposed approach.