1. Introduction

Artificial intelligence is a term that, very paradoxically, holds a prerequisite for the absence of intelligence, which the designer must then overcome. Intelligence can be viewed as the ability of a system to perceive its environment, reason upon the acquired knowledge and perform an action or task based on this information to meet its objective.

When the possible states of the environment are predictable the designer can create an intelligent system that performs well for all possible situations. However the world is a messy place

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and more often than not the environment is unpredictable and knowing or encoding such information into the system a priori is impractical.

The horizontal coordinates of the UAV (x, y) can be controlled using an outer loop on this inner loop but that is not covered here, we shall assume that the environment of the quadrotor is

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

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

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The quadrotor model that has been used is based on work done by Bouabdallah et al. [2]. Following Bouabdallah, the earth-fixed frame E and the body-fixed frame B are as seen in Figure 1. The aerodynamic forces and moments considered in the model are based on the

A dynamic model for the quadrotor is used for the purpose of simulation in this chapter so that

the control strategy that has been presented here can be thoroughly evaluated.

The quadrotor parameters are based on the work of Bouabdallah [4] (Table 1).

m 0:53 kg Mass

c 0:04 m Chord

θ<sup>0</sup> 0:26 rad Pitch of incidence θtw 0:05 rad Twist pitch a 5:70 — Lift slope

Cd 0:05 — Airfoil drag coefficient Ac 0:01 m<sup>2</sup> Helicopter center hub area

r 1:29 kg=m<sup>3</sup> Air density <sup>ϑ</sup> <sup>1</sup>:<sup>80</sup> <sup>10</sup><sup>5</sup> Pa:<sup>s</sup> Air viscosity <sup>V</sup> <sup>3</sup>:<sup>04</sup> <sup>10</sup><sup>4</sup> <sup>m</sup><sup>3</sup> Volume

Design Variable Value Units Description

L 0:23 m Craft diameter Jr <sup>6</sup> <sup>10</sup><sup>5</sup> kg:m<sup>2</sup> Rotor inertia

Ixx <sup>6</sup>:<sup>23</sup> <sup>10</sup><sup>3</sup> kg:m<sup>2</sup> Moment of inertia along <sup>x</sup> Iyy <sup>6</sup>:<sup>23</sup> <sup>10</sup><sup>3</sup> kg:m<sup>2</sup> Moment of inertia along <sup>y</sup> Izz <sup>1</sup>:<sup>12</sup> <sup>10</sup><sup>2</sup> kg:m<sup>2</sup> Moment of inertia along <sup>z</sup> <sup>b</sup> <sup>3</sup>:<sup>13</sup> <sup>10</sup><sup>5</sup> <sup>N</sup>:s<sup>2</sup> Thrust factor in hover <sup>d</sup> <sup>7</sup>:<sup>50</sup> <sup>10</sup><sup>5</sup> <sup>N</sup>:s<sup>2</sup> Drag factor in hover N 2 — Number of blades R 0:15 m Propeller radius

boundless in all directions except the datum in z, i.e. the ground.

work of Gary Fay as in Ref. [3].

2.1. Quadrotor parameters

Table 1. Quadrotor parameters [4].

The set of actions of a system and its objective, on the other hand, is usually known a priori, so it logically follows that one should design a system that should be able to learn how to deal with new situations to meet its objective given the limited set of actions.

Formally, the system or agent should improve its performance as measured by a metric (P) on a task (T) with increasing experience (E). This brings us to machine learning (ML).

At this point, let us note that an adaptable control system is one that modifies the control law so that the system remains stable and the control objective is met.

Whether one looks at it from the perspective of ML, in that the system is initially poor at meeting the objective and hence it changes system parameters to improve or from the control theory perspective that the environment or system has changed and the control objective is not met demanding a change in the control law, we are describing a similar situation.

This chapter is written using the attitude and altitude controller of a quadrotor unmanned aerial vehicle (UAV) as a running example however every idea will be presented generally at first and then tied back to the practical example in consideration.
