1. Lecture 3: Common Simple Dynamic Systems

2. Common simple dynamic systems - First order -Second order
Outline of the lesson.
Common simple dynamic systems
- First order -Second order
- Dead time - (Non) Self-regulatory

3. When I complete this chapter, I want to be able to do the following.
Predict output for typical inputs for common dynamic systems

4. 3.1. First-Order Differential Equation Models
The model can be rearranged as
where τ is the time constant and K is the steady-state gain.
The Laplace transform is

5. 3.1.1. Step Response of a First-Order Model
In the previous lecture, we have learned about the step response of first order systems.

6. 3.1.2. Impulse Response of a First-Order Model
Consider an impulse input, u(t) = δ (t), and U(s) = 1; the output is now
The time-domain solution is
which implies that the output rises instantaneously to some value at t = 0 and then decays exponentially to zero.

7. Integrating Process
When the coefficient a0 = 0 in the first order differential equation , we get
where K = (b / a1). Here, the pole of the transfer function G(s) is at the origin, s = 0.

8. Integrating Process
The solution of the Equation , could be written immediately without any transform as
This is called an integrating (also capacitive or non-self-regulating) process. We can associate the name with charging a capacitor or filling up a tank.

9. SIMPLE PROCESS SYSTEMS: INTEGRATOR
pump
valve
Level sensor
Liquid-filled
tank
Plants have many inventories whose flows in and out do not depend on the inventory (when we apply no control or manual correction).
These systems are often termed “pure integrators” because they integrate the difference between in and out flows.

10. SIMPLE PROCESS SYSTEMS: INTEGRATOR Plot the level for this scenario
pump
valve
Level sensor
Liquid-filled
tank
Plot the level for this scenario
Fin
Fout
time

11. SIMPLE PROCESS SYSTEMS: INTEGRATOR
pump
valve
Level sensor
Liquid-filled
tank
Level
Fin
Fout
time

12. SIMPLE PROCESS SYSTEMS: INTEGRATOR
pump
valve
Level sensor
Liquid-filled
tank
Let’s look ahead
to when we
apply control.
Non-self-regulatory variables tend to “drift” far from desired values.
We must control these variables.

13. 3.2. Second-Order Differential Equation Models
We have not encountered examples with a second-order equation, especially one that exhibits oscillatory behavior.
One reason is that processing equipment tends to be self-regulating.
An oscillatory behavior is most often the result of implementing a controller.
For now, this section provides several important definitions.

14. 3.2. Second-Order Transfer Function Models
where ωn is the natural (undamped) frequency, ζ is the damping ratio or coefficient, K is the steady-state gain, and τ is the natural period of oscillation, where τ = 1/ωn.
The characteristic equation is
Which provides the poles

15. SIMPLE PROCESS SYSTEMS: 2nd ORDER10 20 30 40 50 60 70 80
0.2
0.4
0.6
0.8 1
Time
Controlled Variable
Manipulated Variable
100
120
140
160
180
200
0.5
1.5
overdamped
underdamped

16. WORKSHOP
Four systems experienced an impulse input at t=2. Explain what you can learn about each system (dynamic model) from the figures below. 5 10 15 20 25 30 1 2 3
output
(a) -1
(b)
time
(c)
0.5
1.5
2.5
(d)

17. 3.4 Processes with dead time
Many chemical processes involve a time delay between the input and the output.
This delay may be due to the time required for a slow chemical sensor to respond or for a fluid to travel down a pipe.
A time delay is also called dead time or transport lag.
In controller design, the measured output will not contain the most current information, and hence systems with dead time can be difficult to control.

18. Let’s learn a new
dynamic response
& its Laplace
Transform
Let’s consider plug flow through a pipe. Plug flow has no backmixing; we can think of this a a hockey puck traveling in a pipe.
What is the dynamic response of the outlet fluid property (e.g., concentration) to a step change in the inlet fluid property?

19. THE FIRST STEP: LAPLACE TRANSFORM
 = dead time 
What is the value of
dead time for
plug flow?
Xout
Xin
time

20. THE FIRST STEP: LAPLACE TRANSFORM
Is this a
dead time?
What is the
value? 1 2 3 4 5 6 7 8 9 10
-0.5
0.5
time
Y, outlet from dead time
X, inlet to dead time

21. THE FIRST STEP: LAPLACE TRANSFORM
Our plants have
pipes. We will
use this a lot!
The dynamic model for dead time is
The Laplace transform for a variable after dead time is

22. Pade approximation of the time delay
There several methods to approximate the dead time as a ratio of two polynomials in s.
On such method is the first-order Pade approximation.

23. Example 3.2
Use the first-order Pade approximation to plot the unit-step response of the first order with a dead-time function: Making use of the dirst order Pade approximation, we can construct a plot with the approximation

24. Matlab code
th = 3; P1 = tf([-th/2 1],[th/2 1]); % First-order Padé approximation t = 0:0.5:50; taup = 10; G1 = tf(1,[taup 1]); y1 = step(G1*P1,t); % y1 is first order with Padé approximation of % dead time y2 = step(G1,t); t2 = t+th; % Shift the time axis for the actual time-delay function plot(t,y1,t2,y2,’r’);

25. The approximation is very good except near t = 0, where the approximate response dips below. This behavior has to do with the first-order Pade approximation, and we can improve the result with a second-order Pade approximation.