Implementation of Flight Control System Based On PID Controller andKalman Filter for UAV

BY

NITISH KOYYALAMUDIK-ID: K00346319

INSTRUCTOR

DR. LIFFORD MCLAUCHLAN

DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

ABSTRACT

Kalman & PID controller for UAV (Unmanned Air Vehicle) speed and pitch angle of formation flightcontrol system design. Drone tuning PID parameters, in order to achieve stability of unmanned aerialvehicle flight control. Simulation results are displayed, PID controller & Kalman has a betterperformance 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 requirementsfor precise control and real-time.

Key words: Kalman Filter (KF), PID Controller, Flight Control System (FCS), Unmanned Air Vehicle(UAV), MATLAB.

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 thewing formation flying, increase in efficiency of operation and decreased energy utilization and It is alsocrucial for better controlled formation flying, including attitude and relative position control of UAVflight. Unmanned aerial vehicle formation flight control system design and realization algorithm andcollision prevention programmes in close cooperation. Unmanned aerial vehicles used in many things:Military Applications, Environmental Protection, Aviation Archeology (measuring pollution in air andmonitoring 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 theinput signal. The amplitudes w(t) of intercession control signals and noise signal v(t). Unmanned AirVehicles 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, lateraland vertical) distance between. Kalman and PID combination by static error can be eliminated. Itensures that the system design is simple, robust and reliable applied to PID. Kalman filter used to filtersignal detection and extraction of feedback and real signal noise.

PID controller calculation error as measured process variable and the difference between the desiredset point. PID can solve existing problems, improving the dynamic response of Unmanned AirVehicles. The objective of this study is to design using Kalman, PID combination to study theregulation of quality changes in disturbance of strong noise, adjust the control of PID controllers, usingKalman filter to eliminate noise or intercession with Unmanned Air Vehicles light control system forunmanned aerial vehicle formation flight stability.

Figure.1. PID Control System and Kalman filter

2. MATHEMATICAL MODEL FOR UNMANNED AIR VEHICLE

In this paper the Bluebird contains longitudinal motion is investigated. The Unmanned Air Vehiclemodel 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, wVertical 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.

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 ofthe plane, define a positive deflection of the elevator. Fluctuations and short-period oscillations causedby longitudinal transfer functions the following approximation. Hanging oscillation occurs in the nearlyconstant 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, asadvocated in the x direction helps to speed changes. Short-period there is a very good agreement, shortcycle 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:

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Short cycle patterns occur in smaller time periods where the pitch and the change of the angle of attackis significant, and high damping factor. Vertical roots showed steady motion. Short cycles are morestable 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 Vehiclesplatform. PID controller with optimal and robust limitations. In addition, it is also hard to optimizeparameters, and in some cases. PID control by linear combination of the basis of the basic idea is tocontrol the objects charged proportional, integral, differential coefficient. By using PID control systemperformance depends on 3 proper parameters and law of PID control is

7

Where kP is scale factor, TI is the integral constant of time andTD is the derivative time constant.

The PID controller with good results, in control, but in the special circumstances of high speed andhigh-altitude flight, the air flow, pressure, temperature could lead to a dramatic disturbance. Practicehas proved that the PID controller does not properly complete control in this case, mainly in flight, theneed for strict quality control requirements.

5. KALMAN FILTER OF UNMANNED AIR VEHICLE ESTIMATION

If the signal and noise of random multiple-dimensional non-stationary random process, its timevariation and not a fixed power spectrum makes it difficult to automatically adjust the PID controllerparameters does not achieve the desired effect. KF is used to filter is to remove noise and exact realsignals feedback signal noise is detected.

KF is used not only to calculate approximately the smooth scalar system, but also to give unbiasedestimating to the multi input and multi output (non-steady) system. Additionally, the KF algorithm is arepeat algorithm, particularly suitable for run on computer system. KF uses initial values and statespace matrices to calculate the residue, gain values and to calculate approximately the value of realsignal.

Step KF can be used for (LDS) Linear Discrete State Equation is:

Wherex(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, andG 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, andv(k) Noise Measurement Vector with Mean of Zero & known Covariance.

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

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UAV model for the Bluebird short period longitudinal equation given as follow:

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Interference characteristics of Gaussian white noise generated by the Matlab command applied toBluebird drone in which actual values. Then KF technique and its effectiveness. Real interference isusually in the control process and an influence on the controller. Therefore, using a filtering techniqueis important. You can calculate values for the States. Disturbance, of course, must first be determinedand applied to the system. At last, KF can be applied to a disturbing development and effective controlsystem. Matlab code to do this. Root locus portrait transfer function is shown in Figure.3, alleigenvalues in the left plane, so stable is the system.

Figure.3

: 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 controlunmanned aerial vehicle models, will result in a little interference with the PID controller. Followingfigure 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 rollrates of bluebird model of Unmanned Air Vehicle.

Figure.5: Distinguish between Actual and Estimate values for Pitch rate, Yaw rate and rollrates(EKF)

UKF can have more precise than Extended Kalman Filter. Thereby, the Unscented Kalman Filter forpitch have almost same value between the estimate and Actual values, yaw rates and roll rates ofbluebird model for Unmanned Air Vehicle are estimated.

Figure.5: Distinguish between Actual and Estimate values for Pitch rate, Yaw rate and rollrates(UKF)

Kalman filtering technique described in this paper modeling and PID control for Unmanned Air Vehiclesystems. 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 beenused a number of different technologies. Both linear and non-linear control technology for model-basedcontrol. In this article, the PID control technology for design of Unmanned Air Vehicle models. VerticalUnmanned Air Vehicle control control mathematical model of movement can be evaluated, andUnmanned Air Vehicle control model controlled by PID control technique. PID control technique canbe used to formations. Due to the control of noise pollution caused by the deterioration of the quality ofsignal, which ensure minimal overshoot control system. Finally, compared to the Unscented KalmanFilter 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 ExtendedKalman filtering .

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 precisecontrol. The simulation results show moved forward control and vertical movement control. When theUnmanned Air Vehicle receiving input to move forward when you move the throttle, the thrust willresult 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 intotraditional PID control system for airplane formation flew because of higher reliability and greatervalue 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.

8. REFERENCES

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