High-speed AUV motion control method

Abstract

The invention discloses a a high-speed autonomous underwater vehicle (AUV) motion control method which is characterized in that a radial basis function neural network and a boundary layer method are integrated into traditional sliding mode control, so that the control precision of the AUV during high-speed motion is improved, and buffeting of a control system is effectively inhibited. The high-speed AUV motion control method comprises simplification of a high-speed AUV motion model and design of the motion control method, and compared with a traditional AUV model simplification method, an adaptive change parameter model is added during model simplification so that the established model is closer to the actual situation; compared with a traditional AUV sliding mode control method, compensation of the radial basis function neural network is added in sliding mode control, the switching face of the sliding mode is improved through the boundary layer method, and thus the sliding mode control system can keep high control precision and restrain buffeting.

Description

technical field [0001] The invention relates to the field of autonomous underwater vehicle (AUV, Autonomous Underwater Vehicle) control, in particular to a high-speed AUV motion control method. Background technique [0002] Autonomous Underwater Vehicle (AUV) is one of the powerful tools for exploring ocean space. AUV has been widely used in military marine technology, oil field survey, seabed salvage, pipeline maintenance, seabed survey and other fields. However, traditional underwater robots generally have problems such as low speed and poor environmental adaptability. Focusing on the urgent needs of deep-sea rapid emergency search and rapid underwater environment assessment, it is important to explore motion control methods for high-speed AUVs. direction. [0003] PID control (Proportion Integration Differentiation Control), back stepping control (Back Stepping Control), fuzzy control (Fuzzy Control), sliding mode control (Sliding Mode Control), neural network control (N...

AI Technical Summary

Problems solved by technology

PID control can only be applied to simple control of some AUVs under weak maneuvering, but it is sensitive to changes in environmental parameters, and tuning optimization is troublesome; backstepping control relies on accurate mathematical models, but it is difficult to accurately model this type of new high-speed AUV Obtained; although the sliding mode variable structure control has the characteristics of fast response, it is easy to cause chattering; the fuzzy controller relies on prior knowledge; although the neural network control has a strong nonlinear approximation ability, its network layers and The number of nodes in each layer is difficult to determine; deep learning has powerful complex nonlinear modeling capabilities, but its training is time-consuming, and the verification of model correctness is complex and cumbersome

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