UNIVERSITÁ DEGLI STUDI DI SALERNO
Other more complex
feedback control structures
Prof. Ing. Michele MICCIO
•Dip. Ingegneria Industriale (Università di Salerno)
•Prodal Scarl (Fisciano)
rev. 2.1 of May 15, 2017
see § 7.1.1
Adaptive control
or
Self-Tuning
see § 22.1
A control technique in which one or more parameters of the
PID controller are sensed and used to vary the feedback
control signals in order to satisfy the performance criteria.
•There are many situations where the changes in process dynamics are so
large that a constant linear feedback controller will not work satisfactorily.
For example, the dynamics of a supersonic aircraft.
•Adaptive control is also useful for industrial process control. Since delay
and holdup times depend on production, it is desirable to retune the
regulators when there is a change in production.
•Adaptive control can also be used to compensate for changes due to
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aging and wear.
Inferential Control
 Problem:
see § 22.2
The controlled variable cannot be measured (or has a too large
sampling period).
Possible solutions:
1. Control another related variable (e.g., temperature instead of
composition).
2. Inferential control:
 Control is based on a suitable estimate of the controlled
variable.
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 Seborg, Process_Dynamics_and_Control_2nd-ed_2003
Inferential Control
see § 22.2
4
Inferential Control
Soft sensor
•
Soft sensor or virtual sensor is a common name for software where several
measurements are processed together and then properly used for calculating new
quantities, which need not be measured.
•
Soft sensors are used when hardware sensors are unavailable or unsuitable
•
Soft sensors are inferential estimators, taking advantage of available measurements and
drawing conclusions from process observations
•
Well-known software algorithms that can be seen as soft sensors include Kalman filters.
•
More recent implementations of soft sensors use neural networks or fuzzy computing.
•
Examples:
 Kalman filters for estimating the geographical location
 Soft sensors in the biotech industry:
 to determine total cell mass in a bioreactor
 to estimate the concentration of product proteins inside microorganisms
 Seborg, Process_Dynamics_and_Control_2nd-ed_2003
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 Fortuna, L., Graziani, S., Rizzo, A., Xibilia, M.G., Soft Sensors for Monitoring and Control of Industrial Processes, 2007
Robust control
Robustness
Robust control aims at designing a fixed (non–adaptive) controller such that
some defined level of performance of the controlled system (e.g., closed–
loop stability, reference tracking performance and disturbance rejection
performance) is guaranteed, irrespective of changes in plant dynamics
within a predefined (typically compact) set
• Robust control is a branch of control theory that explicitly deals with
uncertainty in its approach to controller design.
• The early methods of Bode and others were fairly robust; the theory of
Robust Control took shape in the 1980s and 1990s and is still active
today.
• In contrast with an adaptive control policy, a robust control policy is
static; rather than adapting to measurements of variations, the controller
is designed to work assuming that certain variables will be unknown but,
for example, bounded.
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Cascade control
d1
see § 8.2 at pag. 245
ysp
d2
R1
w
R2
u
G2
w
G1
y
see § 20.1
at pag. 395
actual
process
 Two feedback control loops are "nested”:
1. primary or master or external loop
2. secondary or slave or internal loop
 only one manipulated variable u, but more than one measured variable
 w must be a measurable (auxiliary) variable
 the output from the "master” controller R1 becomes the set point for the
"slave” controller R2
 usually, G2 is a minimum phase system
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 G1 can be a non-minimum phase system
Cascade control
Example of a Heat Exchanger with measuring delay
Q
w Ti h i
T0 h 0
Tl
l
e st D
G1 
where
1s  1
Q
Ti
T1
R1
R2
w
tD 
l
v
GQ
GT
G2
To
G1
T1
Secondary temperature T0 loop
See §8.2 in Magnani,
Ferretti e Rocco
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Cascade control
Example of a Cascade Jacketed Reactor
Controlled
variables:
1. Reactor
temperature
2. Coolant output
temperature
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Cascade control
Example of a Cascade Jacketed Reactor
 Two process measurements:
1. the primary or outer measured process variable is still the product stream
temperature
2. the secondary measured process variable is coolant temperature out of the jacket
 Two controllers
 Only one final control element
Notes:
As with all cascade architectures, the output of the primary controller is the set point
of the secondary controller.
The secondary or inner loop controller manipulates the cooling jacket flow rate.
 The benefit of a cascade architecture is improved disturbance rejection.
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Ratio Control
see § 8.5.3 at pag. 260
see § 21.5 at pag. 427
Definition
A control scheme the objective of which is to maintain the ratio of two variables
at a pre-specified value.

A ratio control system is characterized by the fact that variations in the
secondary variable don’t reflect back on the primary variable.
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see § 8.5.3 at pag. 260
Ratio Control
Typical applications
Diluition
Comp. A
P&ID schemes
Mixing
Componente A
FT
FC
FT
FF
FF
FT
FC
FT
FC
Comp. B
 ...
 Holding the fuel-air ratio in a burner to
the optimum.
 Maintaining a stoichiometric ratio of
reactants of a reactor.
 Keeping a specified reflux ratio for a
distillation column, etc.
 http://instrumentationandcontrollers.blogspot.it
Comp. B
a)
b)
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see § 21.5 at pag. 427
see § 8.5.3 at pag. 260
Ratio Control
Control strategy
There are at least two possible ways to implement the ratio control strategy:
Ratio computation
FT
wA
wB/wA
%
RIC
FY
wB
where:
FT
"wild" stream A
r = wB/wA
is the setpoint
stream B

• The flow rate of one of the streams feeding the mixed flow, designated as the wild feed, can change freely.
• The ratio controller manipulates a second variable to maintain the desired ratio between the first and the
second variable.
Set-point (wB) computation
wA
K
wB
R2
G2
wB
where:
K = wB/wA
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Introduction to Process Control
Romagnoli & Palazoglu
Multivariable control
Example: 2x2 (DIDO) open loop system
see § 8.6 at pag. 265
see ch. 23 and 24
u
G 11
y
1
1
G 21
G 12
u
2
G 22
y
2
 1
G11 ( s) G12 ( s)   s  1
G( s)  


G21 ( s) G22 ( s)  1
 s 1
2 
s  3
1 

s 1
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Multivariable control
Example: 2x2 (DIDO) closed loop system
y sp1
R1
u1
G 11
y1
G 21
G
y sp2
R2
u2
12
G 22
y2
 Loop interaction may enhance instability
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Multivariable control
Example of a DIDO system:
three way mixing valve
see § 8.6 at pag. 265
A
AB
Controlled variables:
1. output flowrate
2. output composition
B
P&ID scheme
FC
AC
AT
A
x
B
FT
C
 Loops are separated:
no loop interaction!
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Multivariable control
Example of a DIDO system:
Binary Distillation Column
Controlled variables:
1. top composition
2. bottom composition
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