2e1242 project course automatic control - the helicopter
DESCRIPTION
2E1242 Project Course Automatic Control - The Helicopter. The team. David Höök Henric Jöngren Pontus Olsson Ksenija Orlovskaya Vivek Sharma. Resources. Helicopter with two degrees of freedom (Humusoft) Input voltage to two DC motors driving the main and tail propellers (MIMO-system) - PowerPoint PPT PresentationTRANSCRIPT
2E1242 Project Course Automatic Control
- The Helicopter
The team David Höök Henric Jöngren Pontus Olsson Ksenija Orlovskaya Vivek Sharma
Resources Helicopter with two degrees of freedom (Humusoft) Input voltage to two DC motors driving the main and
tail propellers (MIMO-system) Output horisontal and vertical angles Labview (communicating with process) Matlab (simulation, model validation)
The challenge MIMO system under influence of cross-coupling
Modelling Many non directly measurable parameters Subsystems interlinked through many parameters
Main objectiveThe helicopter is supposed to:
Follow a prespecified trajectory that illustrates its performance limitations
Attenuate external disturbances Hair-drier simulating hard wind Change of mass centre - adding a load to helicopter
ModellingHelicopter divided into subsystems
Main motor and vertical movement Tail motor and horisontal movement
Cross coupling:
Main motor to horisontal movement (reaction torque) Horisontal movement to vertical movement (gyroscopic
moment) Cross coupling from tail motor reaction to vertical
moment and vertical gyro effects neglected.
ModellingMain motor and vertical movement
ModellingTail motor and horisontal movement
ModellingPhysically derived differential equation model
Modelling
Black box First approach
subsystem and model are compared
ModellingWhite box / Grey box Measure parameters corresponding to the physical
model. Weight, distances
Determine non directly measurable parameters Frictions, inertias, gyro, reaction torque – iteratively by adjusting
parameters from model to fit responses from process ’ Time constants for motor dynamics
Adjusting curves to static measurement data Functions mapping insignals to pull force, rotor velocity and
reaction torque
Simulink model, vertical
Simulink model, horisontal
Simulink model,reaction torque
Simulink model, gyroscopic moment
Validation, vertical movementStep response of verticalmovement in model and process
t
1
Validation, horisontal movementStep response of horisontal movement in model and process
t
2
Validation, reaction torqueResponse in horisontal movement from step in main motor
t
t
2
1
Validation, gyroscopic effectResponse in vertical movement from step in tail motor
2
1
t
Validation, total model System too unstable to be validated open-loop Two manually tuned PID-controllers are used
Model Process
ModellingConclusion – what have we learned about modelling?
More difficult than expected Dependent system
Tuning a parameter of one subsystem will affect the behavior of other subsystems.
Must find good balance between the best approximation of the separate subsystems and the performance of the total system.
When is the model good enough? – When it is fulfilling its purpose White box: more insight and understanding of system than Black box Black box: less time consuming than white box
ControlDifferent controllers
Manually adjusted PID – one for each degree of freedom LQ controller with observer – one for the total system
Is it necessary to spend weeks modelling if a quickly tuned P.I.D. can solve the control problem?
-The manually adjusted PID against the model dependent
LQ…
Control
PID_vert G_vertu_vert(t)e_vert(t)r_vert(t) y_vert(t)
+-
PID_hor G_horu_horizontal(t)
e_hor(t)r_hor(t)
y_hor(t)
+-
K2
Introducing cross gain – elimination of cross coupling
Conclusion…
Cross gainsK1
+
Validation, vertical movementStep response of verticalmovement in model and process
t
1
Control LQ with observer -
Not all states measureable - introducing state observer
2
1
)()()()()()(vtDutCxtyvtButAxtx
)()()( trtLxtu
))(ˆ)(()()(ˆˆ txCtyKtButxAx
)()(ˆ)( trFtxLtu r
Observer
Helicopter+
-L
Frr(t) u(t) y(t)
)(ˆ tx
)()()()(min 21 tuQtuteQte TT
Control
2
1
vv
212
121
RRRR
T White noise with intensities:
2121
12
,,,
0
RRQQ
R
:Design variables
:No covariance between the noise
•Model linearized by hand•Equilibrium point taken from real process (input voltages and angles)
Control
Singular values
ControlLQ PID
Control PID
Easy and fast to derive and implementPossible to tune without modelling in some casesCompansates for static error caused by hair-drierAble to attenuate static error caused due to change in mass pointDo not reduce cross coupling satisfactory
LQ with observerModel dependent Better performace for a MIMO system with cross couplingLess oscillationsAlmost no overshootCouldn’t attentuate static error caused due to change in mass point very well Many parameter need to be estimated. More complicated to derive and implement
ControlConclusion – what have we learned about control?- Different regulators: PID, LQ ,close look at
advantages and disadvantages over each other.
- The functions are fulfilling their purposes.
THE END…11/5 kl. 03.12