Chapters authored
Model Reference Adaptive Control of Quadrotor UAVs: A Neural Network Perspective
Adaptive models and online learning are two equivalent topics under different umbrellas of research – control systems and machine learning. This chapter will tackle one such application of a neural network-based model reference adaptive controller on a quadrotor unmanned aerial vehicle while stating the general principles behind each design decision so the knowledge can be generalized to other practical applications. The application-oriented presentation of this chapter will run parallel to most research and development processes in the field, where the physical system or a simulator is usually available and a simple control system (such as PID) has already been implemented as a baseline. The black-box nature of a neural network can truly be leveraged to improve performance after reading this chapter. Several practical considerations when approaching such a problem have been discussed together with their general and implemented solutions. The simulation results for the problem have been presented to demonstrate the success of this control strategy.
Part of the book: Adaptive Robust Control Systems