Study on control methods based on identification of unmanned vehicle model

Abstract

This paper presents the results of nonlinear model identification of an unmanned vehicle in the Gazebo simulator based on a neural network autoregressive model. The dynamic characteristics of the control object can vary significantly that complicates the problem. The training sample of movement along the spatial path was obtained at the Robotics Center of the FRC CSC RAS. The model parameters were found by the particle swarm optimization method. Using the identified model, real-time control methods were experimentally compared. For a more accurate assessment of the methods, the model was subjected to random disturbances, and the tracking path of the control object was significantly complicated.