1. Implementation of Flight Control System Based On PID Controller and
Kalman Filter for UAV
BY
NITISH KOYYALAMUDI
K-ID: K00346319
INSTRUCTOR
DR. LIFFORD MCLAUCHLAN
DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

2. ABSTRACT
Kalman & PID controller for UAV (Unmanned Air Vehicle) speed and pitch angle of formation flight
control system design. Drone tuning PID parameters, in order to achieve stability of unmanned aerial
vehicle flight control. Simulation results are displayed, PID controller & Kalman has a better
performance than traditional design, are more accurate and easier to implement, and so on. Meanwhile,
it will significantly improve the performance. In addition, PID control and Kalman is better than
(SC)Short Circuit transfer, Stability, anti-Jamming ability, better Control, it also meets the requirements
for precise control and real-time.
Key words: Kalman Filter (KF), PID Controller, Flight Control System (FCS), Unmanned Air Vehicle
(UAV), MATLAB.

3. 1. INTRODUCTION
Unmanned Air Vehicles for the major battlefield of the war with few casualties of war, better hide,
flexible is more and reduce military spending and It is to increase in hit rate, reducing the drag of the
wing formation flying, increase in efficiency of operation and decreased energy utilization and It is also
crucial for better controlled formation flying, including attitude and relative position control of UAV
flight. Unmanned aerial vehicle formation flight control system design and realization algorithm and
collision prevention programmes in close cooperation. Unmanned aerial vehicles used in many things:
Military Applications, Environmental Protection, Aviation Archeology (measuring pollution in air and
monitoring of forest ) and analysis of Traffic Congestion.
On Figure.1, yv output signal noise and output signal ye is modified Kalman filter. Step signal is the
input signal. The amplitudes w(t) of intercession control signals and noise signal v(t). Unmanned Air
Vehicles was flying aircraft in the absence of the on-board pilot and it is a multiple-input, multiple-
output as well as nonlinear systems. Drone is crucial to keep the wing and lead aircraft (anterior, lateral
and vertical) distance between. Kalman and PID combination by static error can be eliminated. It
ensures that the system design is simple, robust and reliable applied to PID. Kalman filter used to filter
signal detection and extraction of feedback and real signal noise.
PID controller calculation error as measured process variable and the difference between the desired
set point. PID can solve existing problems, improving the dynamic response of Unmanned Air
Vehicles. The objective of this study is to design using Kalman, PID combination to study the
regulation of quality changes in disturbance of strong noise, adjust the control of PID controllers, using
Kalman filter to eliminate noise or intercession with Unmanned Air Vehicles light control system for
unmanned aerial vehicle formation flight stability.
Figure.1. PID Control System and Kalman filter

4. 2. MATHEMATICAL MODEL FOR UNMANNED AIR VEHICLE
In this paper the Bluebird contains longitudinal motion is investigated. The Unmanned Air Vehicle
model variable vertical and longitudinal model as follows in figure.2:
1
Figure.2: Longitudinal variables for UAV model
Where
δe Elevator inputs,
u  Forward Velocity,
wVertical Velocity,
q Pitch Rate,
θ Pitch Angle and
{Xu, Xw, Xq, Zu, Zw Zq, Mu, Mw, Mq } and {Xδe, Zδe, Mδe}  derivatives of dimensional stability.

5. 3. TRANSFER FUNCTION(TF) FOR UNMANNED AIR VEHICLE
We consider speed when flying straight and level flight of an airplane. To get the transfer function of
the plane, define a positive deflection of the elevator. Fluctuations and short-period oscillations caused
by longitudinal transfer functions the following approximation. Hanging oscillation occurs in the nearly
constant angle α. Because the fugoid oscillation mode of long-period, θ becomes very slow, therefore,
inertial forces are negligible. Hanging approximation is not a satisfactory simulation.
Within a short period oscillations occur in almost constant velocity u and the change of angle α, as
advocated in the x direction helps to speed changes. Short-period there is a very good agreement, short
cycle near the natural frequency of vibration and it has more accuracy than the fugoid oscillation.
Therefore, the short term approach to select the aircraft lift passes function of impersonation.
Vertical movement of the short period of the UAV is represented as follows.
2
Formula of short period of Bluebird UAV is as follow
3
The shorter time period that can be approximated by a function vertical transfer function
4
Steering inertia model transfer function is used is
5
This pitch can be drawn the transfer function of the open-loop system is as below:

6. 6
Short cycle patterns occur in smaller time periods where the pitch and the change of the angle of attack
is significant, and high damping factor. Vertical roots showed steady motion. Short cycles are more
stable than fugoid.
4. CONTROLLER DESIGN FOR THE UNMANNED AIR VEHICLE MODEL
Most commercial autopilot using the PID controller. Due to ease of use small Unmanned Air Vehicles
platform. PID controller with optimal and robust limitations. In addition, it is also hard to optimize
parameters, and in some cases. PID control by linear combination of the basis of the basic idea is to
control the objects charged proportional, integral, differential coefficient. By using PID control system
performance depends on 3 proper parameters and law of PID control is
7
Where
kP is scale factor,
TI is the integral constant of time and
TD is the derivative time constant.
The PID controller with good results, in control, but in the special circumstances of high speed and
high-altitude flight, the air flow, pressure, temperature could lead to a dramatic disturbance. Practice
has proved that the PID controller does not properly complete control in this case, mainly in flight, the
need for strict quality control requirements.

7. 5. KALMAN FILTER OF UNMANNED AIR VEHICLE ESTIMATION
If the signal and noise of random multiple-dimensional non-stationary random process, its time
variation and not a fixed power spectrum makes it difficult to automatically adjust the PID controller
parameters does not achieve the desired effect. KF is used to filter is to remove noise and exact real
signals feedback signal noise is detected.
KF is used not only to calculate approximately the smooth scalar system, but also to give unbiased
estimating to the multi input and multi output (non-steady) system. Additionally, the KF algorithm is a
repeat algorithm, particularly suitable for run on computer system. KF uses initial values and state
space matrices to calculate the residue, gain values and to calculate approximately the value of real
signal.
Step KF can be used for (LDS) Linear Discrete State Equation is:
Where
x(k)  State Vector ,
A  Transition Matrix,
u(k)  Input Vector,
B  Control Distribution Matrix,
w(k)  Gaussian Random Noise with Mean of Zero & known Covariance, and
G Transition Matrix of the System Noise.
Measurement will be described in the equation as
8
Where
y(k)  measurement vector,
x(k)  State Vector,
H  measurement matrix, and
v(k)  Noise Measurement Vector with Mean of Zero & known Covariance.

8. 6. SIMULATION FOR KALMAN AND PID CONTROLLER
Vertical motion can be described by the Euler method based on the short cycles of longitudinal motion.
New matrices A and B, the method used to filter can be found in
9
UAV model for the Bluebird short period longitudinal equation given as follow:
10
Interference characteristics of Gaussian white noise generated by the Matlab command applied to
Bluebird drone in which actual values. Then KF technique and its effectiveness. Real interference is
usually in the control process and an influence on the controller. Therefore, using a filtering technique
is important. You can calculate values for the States. Disturbance, of course, must first be determined
and applied to the system. At last, KF can be applied to a disturbing development and effective control
system. Matlab code to do this. Root locus portrait transfer function is shown in Figure.3, all
eigenvalues in the left plane, so stable is the system.
Figure.3

9. : Characteristic Roots are in Left Half Plane of Longitudinal Control System (LCS)
Without the use of a filtering technology, objects that can be controlled by PID controller to control
unmanned aerial vehicle models, will result in a little interference with the PID controller. Following
figure shows Unmanned Air Vehicle model with error causation.
Figure.4: Step response in PID without Filter in Longitudinal Control
KF as an optimal observer is estimating the new values of the states correctly and decreasing error.
EKF cannot have for pitch the same value between the actual and estimated values, yaw rates and roll
rates of bluebird model of Unmanned Air Vehicle.
Figure.5: Distinguish between Actual and Estimate values for Pitch rate, Yaw rate and roll
rates(EKF)

10. UKF can have more precise than Extended Kalman Filter. Thereby, the Unscented Kalman Filter for
pitch have almost same value between the estimate and Actual values, yaw rates and roll rates of
bluebird model for Unmanned Air Vehicle are estimated.
Figure.5: Distinguish between Actual and Estimate values for Pitch rate, Yaw rate and roll
rates(UKF)
Kalman filtering technique described in this paper modeling and PID control for Unmanned Air Vehicle
systems. The entire autopilot system consists of State observer, State estimation and flight controllers,
and several sections. Unmanned Air Vehicle control, especially unmanned helicopter control has been
used a number of different technologies. Both linear and non-linear control technology for model-based
control. In this article, the PID control technology for design of Unmanned Air Vehicle models. Vertical
Unmanned Air Vehicle control control mathematical model of movement can be evaluated, and
Unmanned Air Vehicle control model controlled by PID control technique. PID control technique can
be used to formations. Due to the control of noise pollution caused by the deterioration of the quality of
signal, which ensure minimal overshoot control system. Finally, compared to the Unscented Kalman
Filter of linear state space models and non-linear measurement compares the relative accuracy.
Unscented Kalman Filter was caused by this conclusion more robustness than estimate of Extended
Kalman filtering .

11. 7. CONCLUSION
Using combinations of Kalman & PID made small overshoot, short circuit transfer, stability, anti-
jamming using Kalman & PID combination. Unmanned Air Vehicle can avoid collisions and precise
control. The simulation results show moved forward control and vertical movement control. When the
Unmanned Air Vehicle receiving input to move forward when you move the throttle, the thrust will
result because of the control of throttle. Therefore, the output will display the distance forward velocity.
When the Unmanned Air Vehicle reached the input of elevator, output will drop due to pitch rate.
Control quality deterioration, which ensure minimal overshoot control system. Kalman filter into
traditional PID control system for airplane formation flew because of higher reliability and greater
value in engineering. It has the advantage, what are the short transition, stability, and anti-interference;
it also meets requirements for precise control and real-time.

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