Research and Development of Selflearning Neural Network Algorithm for Optimal Energy-efficient Autonomous Electrical Unmanned Vehicles Motion Control
Summary of Doctoral Thesis
Aleksandrs Korņejevs, Riga Technical University, Latvia
The Thesis is devoted to the research and development of a method for optimizing the energy consumption of electric unmanned vehicles for optimal energy-efficient control. The control structure of unmanned vehicles is described. A mathematical model of vehicle movement, a mathematical model of a neural network, and a self-learning algorithm for optimal energy consumption using a neural network are described. As a result, a new method was developed for optimizing the energy consumption of an electric unmanned vehicle, providing optimal energy efficiency with changing vehicle parameters, without preliminary calculations and settings.
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