Model Reference Adaptive Control of Quadrotor UAVs: A Neural Network Perspective
Nikhil Angad Bakshi · InTech eBooks · 2018
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.