**Model Reference Adaptive Control of Quadrotor UAVs: A Neural Network Perspective** Model Reference Adaptive Control of Quadrotor UAVs:

DOI: 10.5772/intechopen.71487

Nikhil Angad Bakshi

books/design-control-and-applications-of-mechatronic-systems-in-engineering/embedded-

[4] Narendra KS, Annaswamy AM, editors. Stable Adaptive Systems. 1989th ed. Englewood

[5] Gaiceanu M, Solea R, Codres B, Eni C. Efficient DC drive system by using adaptive control. In: Book Group Author(s): IEEE Conference: International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) Location: ROMANIA Date: MAY 22-24, 2014 Sponsor(s): IEEE Ind Elect Soc; IEEE Ind Applicat Soc; IEEE Power Elect Soc; Transilvania Univ Brasov, 2014 INTERNATIONAL CONFERENCE ON OPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT (OPTIM); MAY 22-24, 2014; Brasov.

[6] Gaiceanu M, Eni C, Coman M. The model reference adaptive control of the DC electric

[7] Filipescu A. Two new adjustment laws for variable structure and compound robust adap-

drive system. Advanced Materials Research. 2014;875-877:2030-2035

tive control. In: SIMSIS9'96; 24-25 Oct 1996; Galati. pp. 53-60

control-of-the-dc-drive-system-for-education

Cliffs, NJ: Prentice Hall; 1989

134 Adaptive Robust Control Systems

Romania: IEEE; 2014. pp. 381-388

Additional information is available at the end of the chapter Nikhil Angad Bakshi Additional information is available at the end of the chapter

A Neural Network Perspective

http://dx.doi.org/10.5772/intechopen.71487

#### Abstract

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 applicationoriented 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.

Keywords: model reference adaptive control, neural networks, UAV, quadrotor, machine learning, robotics, online learning, MLP networks
