Investigation of Internal Model for Unmanned Vehicle Control in Case of Its Aggressive Motion along a Spatial Trajectory

The work is devoted to the study of methods that are used to control the movement of an object along a given trajectory. A control method involving an accurate internal model is proposed. This internal model was built on the basis of the object’s mathematical model and real object, performed by artificial neural networks. For a limited period of time the model is able to determine the object state without surveillance system usage. The dynamic model of an unmanned vehicle was obtained by method developed at the Robotics Center of the FRC CSC RAS. This method acquires experimental data and performs model identification by means of a neural network. The trajectory is a set of spatial points generated by the developed real unmanned vehicle simulator. The control was carried out on the basis of PID-controller and model predictive control method. The comparison of control methods for a real and virtual unmanned vehicles was conducted in the simulator developed. The results of field experiments, during which control by internal model was applied, are presented.