ERGONOMY OF FLIGHT CONTROL
n In the fifties : n How the pilot manages his aircraft ? n What operating image ? n Comparison between: n Human control and Automatic control : SURPRISE ! ….
Jean Piaget (1896 / 1980)
n Swiss Grand Father of Cognitive Psychology
n How the child learns and controls his environment . Four phases ….!
– OPERATING IMAGE – TARGET – Sub TARGET – ACTION – COMPARISON BETWEEN
PREDICTED AND ACHIEVED RESULT
• NATURAL CONTROL : “ YOU DO NOT DRIVE YOUR CAR WITH A PID SCHEME”
-REFERENCE TRAJECTORY
-STRUCTURATION OF THE MV
-ERROR COMPENSATION
4 PIAGET BASIC PRINCIPLES
-INTERNAL MODEL
DEFENCE Mine hunter Ariane 5 attitude control Missile autopilot Laser guided missile (t=62 microsecond…) Gun turrets Radar antennas Infra Red camera High speed Infra Red Missile launch Camera mount Radar antennas Laser mirror Tank turret (T. 55) Mine sweeper auto-pilot Aircraft carrier auto-pilot
AUTOMOTIVE Gear box test bench Dynamic test bench engine Fuel injection Idle fuel injection Clutch antistroke Gear box (tank) Hybrid car (electric-fuel) Air conditioning
METALLURGICAL INDUSTRIES Coating lines aluminium Thickness control - Roll eccentricity Mono-multi-stands Rolling mills
Continuous casting (slab) Push-ovens Coke furnace Hot/cold/Thin rolling mills Steam generators Level, temperature, pressure……;
MISCELLANEOUS (Chem reactors and distillation excluded) Bioreactor Melissa ESA Plastic extrusion robot (high speed) Temperature control of gas furnace Temperature control of TGV train carriage River dam level control (T=1 hour ) Powder milk dryer Electric furnace brazing etc….
FEATURES …the oldest MBPC… 1968 A “few” thousands applications in many fields
FIRST INDUSTRIAL APPLICATIONS OF P.F.C.
uy M
Ts + 1e s -G = P ==θ
y(n) = α y(n-1) + (1- α) . u(n-1-r) . G T
Tsamp- e = α
θ = Tsamp . r Trajectory expo. : λ
1 coincidence point: H
1 Base function: step Target : ΔP(n+H) = (C - yPr) (1 - λH)
Processus with delay : yPr (n) = yP (n) + yM (n) - yM(n-r)
Model : Free mode Forced mode (Liebniz 1674)
My (n) HαHu(n) . GM 1 - α
⎛ ⎞⎜ ⎟⎝ ⎠
Model increment :
M MPH H H(C - (n)) (1 - ) (n) + u(n) GM(1 - ) - (n)y y yr λ α α=
Control equation : H)+(nM H)+(nP Δ=Δ
Increment = Free(n+h) + Forced(n+h)- ymodel(n)
The only mathematical problem for trainees …!
ELEMENTARY TUTORIAL EXAMPLE
PrH(C - (n)) (1 - ) (n)yy Mu(n) + H GMGM(1 - )
λ
α=
0 20 40 60 80 100 1200
20
40
60
80
100
120TEMPERATURE DE MASSE
d°
min
92°
Tmax=108.4°
Tinit=42°
Figure 4
ON LINE ESTIMATOR of DISTURBANCES
Pert
R1
R2
Cons1 MV1 P SP
SP* M ≡ P MV2
Cons2=SP
+
+
B
+
+
Pert* -
+
0 10 20 30 40 50 60 70 80
0
20
40
60
80
100
120 ESTIMATION DE L‘EXOTHERMICITE
Tmasse/ Tmasseestimée
TS-TE
TS
EXOTHERMICITE
min
Figure 3
15
R
T M F i , T i T e
R Reagent
¥
q ( F i ) T M + T M = T i .
1) Fi =ct / MV= Ti CV=TM / level 0 =Ti ? :PFC
2) Ti =ct (!) MV=Fi Parametric Control non linear :PPC
3) MV : Ti and Fi : Enthaplic control (power) :PPC+
4) MV : Pressure of reactor :PFC
4 CONTROL STRATEGIES OF BATCH REACTORS
.
.
❿❿ ❿ ❿ ➗ ❿
➗➗ ➗ ➗ ➗ ➗ ❿ ➗➛ ➛
➖➆ρ Δ➖➦➖➆ρ ρ➖➦
Control PID Degussa / EVONIK
Datum | Name der Präsentation Seite | 17
time
tem
per
atu
re [
°C]
+/- 2 °C
39 different batches with PID
Control PFC Degussa / EVONIK
Datum | Name der Präsentation Seite | 18
time
tem
pera
ture
[°C]
32 different batches with PPC
+/- 0,2 °C
Steel ladle 335 t , 1550 °C
tundish 60 t
Mould 850 - 2200 mm, Cool jacket
SP=70% PFC PFC
Steel level
rolling
Setpoint weight
t Stopper PFC
PI
position detection
nozzle A
A
Weight detection
capteur
IDENTIFICATION
- Ultra-low level test signals ! ! …. « I do want to see your test signal on the level ..!« - Set of harmonics signals Eigen function - High level Parallel filtering .- Different metal alloys
3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9
x 104
-20
-10
0
10
20
30
40
50
60
70
BULGING COMPLEX CONTROL
without with
STOPPER
Amp sinus
Bmp cosinus
Frequency reference
N*0.025 sec
3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9
x 104
-20
-10
0
10
20
30
40
50
60
70
3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9
x 104
-20
-10
0
10
20
30
40
50
60
70
BULGING COMPLEX CONTROL
without
BULGING COMPLEX CONTROL
without with
STOPPER
Amp sinus
Bmp cosinus
Frequency reference
N*0.025 sec
COMPLEX ALGEBRA PREDICTIVE CONTROL ?!
Bâche tamponROO1
Température T0
Vanne Chaud VC
Vanne Froid VF
TransmetteurPression
Température T5
Banc d’essaiTempérature T51
Surpresseur
T 31
T 30
TransmetteurDébit
TransmetteurDébit
PFC CHAUD
Convexité Echangeur Chaud
TEQ = L x T31 + ( 1 – L ) x T0
Prise en tendance de la température d’entrée
F(T0)
PFC FROIDConvexité Echangeur Froid
TEQ = L x T30 + ( 1 – L ) x T51
Prise en tendance de la température d’entrée
F(T51)
Logique de choix de l’action
(Chaud ou Froid)
T5 T0 T31
T5 T51 T30
Consigne de température
Régulation débit chaud
Régulation débit froid
Débit source chaude
Débit source froide
T0
T51
T5
T31
T30
PID
Essai A2si du 17 avril 2012 (673)
15,0
25,0
35,0
45,0
55,0
65,0
18:14:24 18:21:36 18:28:48 18:36:00 18:43:12 18:50:24 18:57:36 19:04:48
Temps (heures)
Tem
péra
ture
(°C
)po
sitio
n va
nne
(%)
PFC Essai A2si du 18 avril 2012 (676)
0,0
20,0
40,0
60,0
80,0
100,0
14:31:12 14:45:36 15:00:00 15:14:24 15:28:48 15:43:12 15:57:36 16:12:00 16:26:24
Temps (heures)
Tem
péra
ture
(°C)
posi
tion
vann
e (%
)
T Inj
Consigne
retard +3 mn
SANOFI VERTOLAY VITRY sur SEINE ARAMON MONTPELLIER KÖLN ELBEUF Training of staff: Transfer of Know How
CODE SCILAB // com_elem_kindergarten tf= 1500; w=1:1:tf; // temps // initialisation u=zeros(1,tf); tech=1; MV=u; CV=u; sm=u; DV=u; SP=u; r=200; // dynamiques du modèle et du processus, // constante de temps, gain : taum=35; am=exp(-tech/taum); bm=1-am; Km=1.7; taup=35; ap=exp(-tech/taup); bp=1-ap; Kp=1.7; TRBF=45; lh=1-exp(-tech*3/TRBF); //----------------------- for ii=2+r:1:tf, if ii<700 then DV(ii)= 0; else DV(ii)=20; end // perturbation SP(ii)=100; // point de consigne CV(ii)=ap*CV(ii-1)+bp*(Kp*MV(ii-1-r)+DV(ii)); // processus sm(ii)=am*sm(ii-1)+bm*Km*MV(ii-1); // modèle spred=CV(ii)+(sm(ii)-sm(ii-r))*1; // prédiction d=(SP(ii)-spred)*lh+sm(ii)*bm; MV(ii)=d/(Km*bm); // variable manipulée // contraintes if MV(ii)>70 then MV(ii)=70; end if MV(ii)>MV(ii-1)+4 then MV(ii)=MV(ii-1)+4; end if MV(ii)<MV(ii-1)-4 then MV(ii)=MV(ii-1)-4; end end //----------------------- scf(1); clf; plot(w,MV,'b',w,CV,'r',w,SP,'r',w,DV,'m'); a=gca(); a.grid=[1,1]; a.tight_limits="on"; a.data_bounds=[1,0;1500,150]; xlabel('sec'); xtitle('MV b, CV et SP r, DV m , R=200 !,.. contraintes : abs=60 / vitesse = 4');
Conclusion
n Dissemination ? : where is the problem?: n The technical and economic efficiency of PFC
is clearly demonstrated on many different processes
n TEACHING PFC: n Continuous education of : n Teachers of technical schools n Industrial operators n Implementation of PFC in all new PLC’s is to be
continued
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