**1. Introduction**

In the past few years, the interest in unmanned aerial vehicle (UAV) has been growing strongly. The possibility of removing human pilots from danger as well as the size and cost of UAVs are indeed very attractive but have to be compared to the performances attained by human-piloted vehicles in terms of mission capabilities, efficiency and flexibility. The design of flight controllers able to offer to UAVs an accurate and robust control is an important step in the design of fully autonomous vehicles. In practical operations, fixed-wing UAVs have been used for years in routine surveillance missions but their lack of stationary flight capability has shifted the focus to vertical take-off and landing (VTOL) vehicles offering the possibility of being launched from virtually anywhere along with the ability to hover above a target. Several designs are available when it comes to VTOL vehicles; however, the quadrotor configuration presented in this chapter offers all the advantages of VTOL vehicles along with an increased payload capacity, a stability in hover inherent to its design (while it is the hardest flight condition to maintain for conventional helicopter) as well as an increased maneuverability [1].

In this work, the vision-based position and altitude tracking control of a quadrotor UAV is considered. This would be then on used to align the drone to the center of a pre-defined landing pad marker on which the quadrotor would autonomously land. In practical missions, the stability of the quadrotor is easily affected by abrupt changes in the input commands. The flight controller that is designed must be capable in offering an accurate and robust control to the quadrotor. The controller demonstrated in this chapter is the sliding mode controller (SMC). The sliding mode control (SMC) technique, being a non-linear control technique, has found great applications in offering robust control solutions for handling quadrotors [2–7]. This chapter will briefly describe the process of implementing a vision algorithm alongside a classical SMC for autonomous landing of the quadrotor on a stationary platform.

The kinematic and dynamic models of a quadrotor will be derived based on a

• The center of gravity of the quadrotor coincides with the body fixed frame origin.

The first step in developing the quadrotor kinematic model is to describe the different frames of references associated with the system. It is necessary to use

1. Newton's equations of motion are given the coordinate frame attached to the

3. On-board sensors like accelerometers and rate gyros measure information with respect to the body frame. Alternatively, GPS measures position, ground

specified in the inertial frame. In addition, map information is also given in an

(*F<sup>b</sup>*). The inertial frame is fixed at a point at ground level and uses the N-E-D notation, where N points towards north direction, E points towards east direction and D points towards earth. On the other hand, the body frame is at the center of quadrotor body, with its *x* axis pointing towards the front of the quadrotor, *y* axis pointing towards the left of the quadrotor and the z axis pointing towards the ground. The vehicle frame has an axis parallel to the inertial frame but has the origin shifted to the quadrotor's center of gravity. Vehicle frame's yaw is adjusted to match the quadrotor's yaw to get the vehicle frame-1 frame which is then pitch adjusted to get the vehicle frame-2. Finally

The transformation from inertial to vehicle frame is just a simple translation. On the other hand, the transformation from vehicle to body frame is given by a rotation

*<sup>v</sup>*2ð Þ *<sup>ϕ</sup> <sup>R</sup><sup>v</sup>*<sup>2</sup>

cosð Þ*θ* 0 � sin ð Þ*θ* 01 0 sin ð Þ*θ* 0 cosð Þ*θ*

¼

2 6 4 *<sup>v</sup>*1ð Þ*<sup>θ</sup> <sup>R</sup><sup>v</sup>*<sup>1</sup>

*<sup>v</sup>* ð Þ¼ *ψ*

2 4

*cθc<sup>ψ</sup> sϕsθc<sup>ψ</sup>* � *cϕc<sup>ψ</sup> cϕsθc<sup>ψ</sup>* þ *sϕs<sup>ψ</sup> cθs<sup>ψ</sup> sϕsθs<sup>ψ</sup>* þ *cϕc<sup>ψ</sup> cϕsθs<sup>ψ</sup>* � *sϕc<sup>ψ</sup>* �*s<sup>θ</sup> sϕc<sup>θ</sup> cϕc<sup>θ</sup>*

cosð Þ *ψ* sin ð Þ *ψ* 0 � sin ð Þ *ψ* cosð Þ *ψ* 0 0 01

3 5

), the vehicle frame-2 (*F<sup>v</sup>*<sup>2</sup>

), the vehicle

3 5 9

>>>>>>>>>>=

>>>>>>>>>>;

(1)

3 7 5

), and the body frame

Newton-Euler formalism with the following assumptions [10]:

these coordinate systems for the following reasons:

• The propellers are rigid.

*DOI: http://dx.doi.org/10.5772/intechopen.86057*

quadrotor.

inertial frame.

), the vehicle frame-1 (*F<sup>v</sup>*<sup>1</sup>

*<sup>v</sup>* ð Þ *ϕ; θ; ψ* , given by:

10 0 0 cosð Þ *ϕ* sin ð Þ *ϕ* 0 � sin ð Þ *ϕ* cosð Þ *ϕ*

*Rb*

3 5

2 4

frame (*F<sup>v</sup>*

matrix *Rb*

2 4

**31**

• The quadrotor structure is assumed to be rigid and symmetrical.

*Vision-Based Autonomous Control Schemes for Quadrotor Unmanned Aerial Vehicle*

• Thrust and drag are proportional to the square of propeller's speed.

2. Aerodynamics forces and torques are applied in the body frame.

speed, and course angle with respect to the inertial frame.

4.Most mission requirements like loiter points and flying trajectories are

In this case, we describe a total of frames, namely: inertial frame (*F<sup>i</sup>*

the body frame is obtained by adjusting the roll of the vehicle frame-2.

*<sup>v</sup>* ð Þ¼ *<sup>ϕ</sup>; <sup>θ</sup>; <sup>ψ</sup> <sup>R</sup><sup>b</sup>*
