Neural Network Algorithm for Intercepting Targets Moving along Known Trajectories by a Dubins’ Car

The task of intercepting a target moving along a rectilinear or circular trajectory by a Dubins’ car is formulated as a problem of time-optimal control with an arbitrary direction of the car’s velocity at the time of interception. To solve this problem and to synthesize interception trajectories, ne...

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Bibliographic Details
Published in:Automation and remote control 2023-03, Vol.84 (3), p.196-210
Main Authors: Galyaev, A. A., Medvedev, A. I., Nasonov, I. A.
Format: Article
Language:English
Subjects:
Algorithms
CAE) and Design
Calculus of Variations and Optimal Control
Optimization
Computer-Aided Engineering (CAD
Control
Control theory
Exact solutions
Interception
Machine learning
Mathematics
Mathematics and Statistics
Mechanical Engineering
Mechatronics
Neural networks
Nonlinear Systems
Robotics
Systems Theory
Time optimal control
Trajectory control
Unsupervised learning
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Description
Summary:The task of intercepting a target moving along a rectilinear or circular trajectory by a Dubins’ car is formulated as a problem of time-optimal control with an arbitrary direction of the car’s velocity at the time of interception. To solve this problem and to synthesize interception trajectories, neural network methods of unsupervised learning based on the Deep Deterministic Policy Gradient algorithm are used. The analysis of the obtained control laws and interception trajectories is carried out in comparison with the analytical solutions of the interception problem. Mathematical modeling of the target motion parameters, which the neural network had not previously seen during training, is carried out. Model experiments are conducted to test the stability of the neural solution. The effectiveness of using neural network methods for the synthesis of interception trajectories for given classes of target movements is shown.
ISSN:0005-1179
1608-3032
DOI:10.1134/S0005117923030049