65656775 model reference adaptive fault damage tolerant control of quadrotor unmanned aerial vehicle...

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UnmannedMini Aerial Vehicles (UMAVs) have found potential applications in military purposes like surveillance,border patrolling, mine detection, crowd control, aerial delivery of payload, civilian purposes like disaster management during floods, earthquakes, fire, commercial missions like aerial photography, television and cinema shootings, and research and development program which need flying vehicles to perform various experiments.

TRANSCRIPT

Model Reference Adaptive Fault/Damage Tolerant Control of Quadrotor Unmanned

Aerial Vehicle (UAV) Course instuctor:

Dr. Youmin Zhang, Dr. Youmin Zhang,

Project by:

Iman Sadeghzadeh

Ankit Mehta

Dept. of Mechanical and Industrial Engineering

Concordia University, Montreal, Quebec, Canada

qIntroductionqModeling the Quad-Rotor UAVØ System model

qModel Reference Adaptive Control (MRAC)Ø MethodsØ MIT rule an structure

Outlines

Ø MIT rule an structure

qSimulation ResultsØ MRAC+LQR

qExperimental ResultsØ Nominal caseØ Fault Case:14.2% loss of the throttle and 15% of Propeller thrust

qConclusion(1)

ØThe advantages of the quad-rotor UAV:• VTOL• Omni-directional flying• Does not require mechanical linkages to vary rotor angle of attack.

• Can be protected by enclosing within a frame (Qball)

Introduction

• Can be protected by enclosing within a frame (Qball)ØMRAC controller advantages

• The MRAC or MRAS is an important adaptive control methodology

• Robustness to some changes of plant parameters and disturbance

• Variety of applications: Aerospace, Chemical, Petrochemical, etc…

(2)

System Model

(3)

Model-Reference Adaptive Systems• The MIT rule• Lyapunov stability theory• Design of MRAS based on Lyapunov stability theory

Different Adaptive Systems

stability theory• Hyperstability and passivity theory• The error model• Augmented error• A model-following MRAS

(4)

MRAC Structure

Model

AdjustmentMechanism

Controller Parameters

ymodel

u

(5)

Controller Plantu yplant

uc

Design controller to drive plant response to mimic ideal response (error = yplant-ymodel => 0)Designer chooses: reference model, controller structure, and tuning gains for adjustment mechanism

The MIT rule

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The MIT rule

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The MIT rule

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Simulation

MRAC and LQR both are set to zero

Simulation

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MRAC is set to zero

Simulation

(9)

LQR is set to zero

Simulation

(10)

MRAC+LQR

Simulation

(11)

Triangle trajectory tracking

Fault-Free Implementation Result

(12)

Square trajectory tracking

Fault-Free Implementation Result

(13)

Triangle trajectory tracking

Fault Diagnosis and Fault-Tolerant Control

(14)

Fault Injection (Implementation)•

14.2 % of Fault in all actuators (Hovering mode 1)

(15)

Fault Injection (Implementation)

14.2 % of Fault in all actuators (Hovering mode 2)

(16)

Fault Injection (Implementation)

Fault injection to back and left motor (Trajectory tracking mode 1)

(17)

Fault Injection (Implementation)

Fault injection to back and left motor (Trajectory tracking mode 2)

(18)

PWM Signals

Fault Injection (Implementation)

(19)

1. Model Reference Adaptive Control forces the dynamic response of thecontrolled plant to approach asymptotically to that of reference model.

2. MRAC and LQR give the best performance to the system.3. The model reference adaptive control minimize the effect of fault on the

system’s behaviour.4. Better result can be obtained with higher adaption rates. However, very

Conclusions

4. Better result can be obtained with higher adaption rates. However, verylarge adaption rate leads to system’s instability.

5. Two types of fault (Throttle loss and propeller loss) showed almost thesame result.

(20)

Thank youThank you

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