A multi-auv synchronous controller structure and design method

Abstract

The invention discloses a multi-AUV synchronous controller structure. The multi-AUV synchronous controller structure is composed of a kinematic controller, a first-order filter, a dynamic controller, a predictor, an approximator and a comparator. A distributed type control structure is adopted, the defect that a controller of an integrated type control structure needs to master all information is overcome, the controller can achieve control over individuals only by mastering the local information, only part of AUVs receive reference target information, it can be ensured that the capacity of information exchange between the multiple AUVs is the minimum, the communication traffic of the information is reduced remarkably, flexibility and operability of the system are improved, and the structure has high fault tolerance and expansibility. The calculation complexity of the multi-AUV synchronous controller structure is remarkably reduced, and the calculation loads of a control algorithm are reduced, so that the controller facilitates real-time calculation of actual microprocessor systems such as single-chip microcomputers and digital signal processors. The multi-AUV synchronous controller structure remarkably improves the speediness and accuracy of the transient approaching effect of a neural network, and therefore the overall control performance of the system is improved.

Description

technical field [0001] The invention relates to the field of autonomous underwater vehicle (AUV) control, in particular to a multi-AUV synchronous controller structure and design method. Background technique [0002] The 21st century is the century of the ocean. The ocean is rich in biological resources and mineral resources, and it is a strategic resource place for the sustainable development of human beings. Autonomous underwater vehicle (AUV) is an important tool for human beings to understand, control, protect and develop the ocean, and its development has always been highly valued by the ocean powers. AUV plays an important role in marine hydrological monitoring, seabed resource exploration, regional search and rescue, submarine cable laying and other fields. Multiple AUVs can meet the needs of AUVs to cooperate in the execution of marine operations, complete complex tasks that a single AUV cannot perform, and significantly improve the efficiency of marine operations. ...

AI Technical Summary

Problems solved by technology

In terms of controller structure and controller design, the prior art has the following deficiencies: First, most of the existing synchronous tracking control algorithms use a centralized control structure, that is, the target information state is globally known

The underwater acoustic channel is one of the most complex wireless communication channels so far. Its inherent characteristics of narrow bandwidth, high noise, and long delay transmission make it difficult for underwater acoustic communication to meet the needs of real-time control of AUV in terms of signal transmission performance.

Due to the limitation of underwater communication bandwidth, the amount of information interaction of AUV cooperation should be reduced as much as possible, and the centralized control structure requires AUV individuals to directly obtain the information of the target state, which significantly increases the amount of information communication, which leads to the centralized control structure in There are certain limitations in practical application

Second, most of the existing AUV controller design methods use the backstepping method. Because the backstepping method needs to derive the virtual control law in the process of each step of recursion, the controller structure is complex and the calculation load is large, which is not conducive to the actual engineering applications

Third, for the dynamic uncertainty and environmental disturbance control problems of AUVs, the existing methods all use direct neural network control. The direct neural network control method performs online learning according to the speed tracking error of the AUV. Since the speed tracking error of the AUV is generally Larger, this will affect the transient approximation performance of the neural network, which will easily cause the controller to fall into saturation, and eventually lead to the reduction of the overall control performance of the system

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