Neurocontrol methods in the context of development of technical solutions for transition to unmanned navigation

Abstract

The paper considers the features and prospects of using neurocontrol methods in the context of development of technical solutions for transition to unmanned merchant vessels. The paper suggests a non-iterative training based artificial neural network (ANN), which is based on the principles of “direct inverse control” to control the speed and motion of unmanned surface vessels. The model is identified, and the structure of an artificial neural network and the diagram of the automatic control system (ACS) of an unmanned vessel (UV) are considered on the example of an electric propulsion vessel. A series of computational experiments is carried out to obtain a sufficiently complete training sample. and the control law is presented. The principle of the control system for an unmanned vessel is considered based on a neural network. At the next stage of the study, focus is on the synthesis of the optimal control system for UV navigation. The problem of the fastest motion of a third-order control object from one point (with any initial speed) to another (at the end point the vessel stops and the speed is zero) is considered. Based on the results of a series of experiments with the UV model, the controller parameters that provide the best indicators of control quality were set in the MATLAB Simulink environment.