System Identification of NN-based Model Reference Control of RUAV during Hover

TL;DR

This paper demonstrates the use of neural networks to identify and control the nonlinear dynamics of a UAV during hover, reducing the need for complex mathematical models and enabling effective control design.

Contribution

It introduces a neural network-based system identification method for UAV control, integrating model reference control with neural networks for nonlinear system modeling.

Findings

Neural network models accurately simulate UAV dynamics during hover.

The approach simplifies control design for complex UAV systems.

Simulation results validate the effectiveness of NN-based control.

Abstract

UAV control system is a huge and complex system, and to design and test a UAV control system is time-cost and money-cost. This paper considered the simulation of identification of a nonlinear system dynamics using artificial neural networks approach. This experiment develops a neural network model of the plant that we want to control. In the control design stage, experiment uses the neural network plant model to design (or train) the controller. We use Matlab to train the network and simulate the behavior. This chapter provides the mathematical overview of MRC technique and neural network architecture to simulate nonlinear identification of UAV systems. MRC provides a direct and effective method to control a complex system without an equation-driven model. NN approach provides a good framework to implement MEC by identifying complicated models and training a controller for it.