Abstract
This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.
Highlights
Vessels in navigation operations may be disturbed by harsh environmental factors such as wind, waves, currents, etc., and violent rolling motions may threaten the life and safety of the vessel itself, which may cause capsizing or other major accidents of vessel destruction and death [1]
An adaptive neural network control scheme for the fin stabilizer of a zero-speed vessel based on a command filter for a large-angle roll of a zero-speed vessel under severe sea conditions is designed by combining auxiliary dynamic system (ADS), a nonlinear disturbance observer, adaptive neural network technology and a command filter backstepping method
Through the fusion design of an RBF neural network and a nonlinear disturbance observer, the design mechanism breaks through the requirements of disturbance observer for the controlled object model knowledge and overcomes the defects of neural network technology that cannot effectively reconstruct external disturbances
Summary
Vessels in navigation operations may be disturbed by harsh environmental factors such as wind, waves, currents, etc., and violent rolling motions may threaten the life and safety of the vessel itself, which may cause capsizing or other major accidents of vessel destruction and death [1]. The traditional PID control strategy is designed based on the Conolly linear model, which cannot have effective system nonlinear characteristics, so it is no longer applicable To this end, scholars have introduced nonlinear control technology methods into the fin stabilizer control of vessel roll and proposed a series of control methods, such as sliding mode control [10,11], model predictive control [12], the Lyapunov direct method [13], and adaptive backstepping control [14]. Based on the above observations, this paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy for the vessel rolling control problem in the presence of input saturation, dynamic uncertainty and external unknown disturbance.
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