 Background
 Linear State Space Models
 Introduction to Laplace Transforms
 Transfer Functions
 FirstOrder Behavior
 Integrating System
 SecondOrder Behavior
 LeadLag Behavior
 Poles and Zeros
 Processes with Dead Time
 Padé Approximation for Dead Time
 Converting State Space Models to Transfer Functions
 Matlab and Simulink
 Summary
 References
 Student Exercises
3.3 Introduction to Laplace Transforms
Most control system analysis and design techniques are based on linear systems theory. Although we could develop these procedures using the state space models, it is generally easier to work with transfer functions. Basically, transfer functions allow us to make algebraic manipulations rather than working directly with linear differential equations (state space models). To create transfer functions, we need the notion of the Laplace transform.
The Laplace transform of a timedomain function, f(t), is represented by L[f(t)] and is defined as
The Laplace transform is a linear operation, so the Laplace transform of a constant (C) multiplying a timedomain function is just that constant times the Laplace transform of the function,
The Laplace transforms of a few common timedomain functions are shown next.
Exponential Function
Exponential functions appear often in the solution of linear differential equations. Here
The transform is defined for t > 0 (we also use the identity that e^{x}^{+}^{y} = e^{x}e^{y})
So we now have the following relationship:
Derivatives
This will be important in transforming the derivative term in a dynamic equation to the Laplace domain (using integration by parts),
so we can write
For an nth derivative, we can derive
n initial conditions are needed: f(0),..., f^{(n – 1)} (0)
One reason for using deviation variables is that all of the initial condition terms in Equation (3.10) are 0, if the system is initially at steadystate.
Time Delays (Dead Time)
Time delays often occur owing to fluid transport through pipes, or measurement sample delays. Here we use q to represent the time delay. If f(t) represents a particular function of time, then f(t – q) represents the value of the function q time units in the past.
We can use a change of variables, t* = t – q, to integrate the function. Notice that the lower limit of integration does not change, because the function is defined as f(t) = 0 for t < 0.
So the Laplace transform of a function with a time delay (q) is simply e^{–}^{q}^{s} times the Laplace transform of the nondelayed function.
Step Functions
Step functions are used to simulate the sudden change in an input variable (say a flow rate being rapidly changed from one value to another). A step function is discontinuous at t = 0. A "unit" step function is defined as
and using the definition of the Laplace transform,
so
Similarly, the Laplace transform of a constant, C, is
Pulse
Consider a pulse function, where a total integrated input of magnitude P is applied over t_{p} time units, as shown in Figure 33.
Figure 33. Pulse function.
The function is f(t) = P/t_{p} for 0 < t < t_{p} and f(t) = 0 for t > t_{p}. The Laplace transform is
Impulse
An impulse function can viewed as a pulse function, where the pulse period is decreased while maintaining the pulse area, as shown in Figure 34. In the limit, as t_{p} approaches 0, the pulse function becomes (using L'Hopital's rule)
Figure 34. Concept of an impulse function.
If we denote a unit impulse as f(t) = d, then the Laplace transform is
Examples of common impulse inputs include a "bolus" (shot or injection) of a drug into a physiological system, or dumping a bucket of fluid or bag of solids into a chemical reactor.
Other Functions
It is rare for one to derive the Laplace transform for a function; rather a table of known transforms (and inverse transforms) can be used to solve most dynamic systems problems.
Table 31 presents solutions for most common functions. If you desire to transform a function from the time domain to the Laplace domain, then look for the timedomain function in the first column and write the corresponding Laplace domain function from the second column. Similarly, if your goal is to "invert" a Laplace domain function to the time domain, then look for the Laplace domain function in the second column and write the corresponding timedomain function from the first column. This notion of the inverse Laplace transform can be written
For example
Initial and FinalValue Theorems
The following theorems are very useful for determining limiting values in dynamics and control studies. The longterm behavior of a timedomain function can be found by analyzing the Laplace domain behavior in the limit as the s variable approaches zero. The initial value of a timedomain function can be found by analyzing the Laplace domain behavior in the limit as s approaches infinity.
Table 31. Laplace Transforms for Common TimeDomain Functions
Timedomain function 
Laplace domain function 

f(t) 
F(s) 
d(t) (unit impulse) 
1 
C (constant) 

f(t  q) 
e^{qs}F(s) 
t 

t^{n} 

sF(S)  f(0) 

S^{n}f(S)  S^{n1}f(0)  L  sf^{(n2)}(0)  f^{(n1)}(0) 

e^{at} 

te^{at} 

1e^{t/}^{t} 

sin wt 

cos wt 

e^{–}^{at} sin wt 

e^{–}^{at}cos wt 

The finalvalue theorem is
The initialvalue theorem is
It should be noted that these theorems only hold for stable systems.
Example 3.2: Application of Initial and FinalValue Theorems
Find the longterm and shortterm behavior of the timedomain function, y(t), using the final and initialvalue theorems on the Laplace domain function Y(s) (we see later that this arises from a step input applied to a secondorder process):
The longterm behavior, y(t → ∞), is found using the finalvalue theorem,
The shortterm behavior, y(t → 0), is found using the initialvalue theorem,
The reader should verify that the timedomain function, y(t), can be found by applying Table 31 to find
and that the values of y(t → ∞), and y(t → 0), are consistent with the final and initialvalue theorems.
General Solution Procedure
To obtain analytical solutions for differential equationbased models, the general procedure is composed of several steps.
Start with an nth order linear differential equation
and known initial conditions
Transform each element of the differential equation to the Laplace domain,
Use algebraic manipulations to solve for the transformed variable.
where N(s) and D(s) are polynomials in s.
Perform a partial fraction expansion to isolate the individual elements.
where a, b, and so forth are the roots of D(s). If roots are repeated the partial fraction expansion has the form (for an example where three roots all have a value of a).
Invert each element back to the time domain to find the final solution for y(t).
For the case of repeated roots (say 3 that have a value of a), the solution has the form
It should be noted that chemical process systems are rarely described by an nth order differential equation. Usually, a set of n firstorder differential equations is transformed to a single nth order equation, as shown in Example 3.3.
Example 3.3: SecondOrder Differential Equation
Consider the following state space model of an isothermal CSTR (Module 5).
where x_{1} and x_{2} represent the concentrations of two components (in deviation variable form) in an isothermal reactor; the initial conditions are x_{1}(0) = x_{2}(0) = 0. Solve for the output (concentration of component 2) response to a unit step input.
The reader should show (see exercise 6) that this can be arranged to a secondorder differential equation,
with the initial conditions y(0) = dy(0)/dt = 0, and the input is initially u(0)= 0.
Taking the Laplace transform of each element,
and substituting back into the differential equation, we find^{1}
Solving for Y(s), we find
Here we consider a unit step input, U(s) = 1/s.
Performing a partial fraction expansion using the roots of the denominator polynomial,
Solve for C_{1} by multiplying by s,
Setting s = 0 to find
Similarly, solve for C_{2} by multiplying Equation (3.20) by s + 2.4053 and setting s = –2.4053
Also, solve for C_{3} by multiplying Equation (3.20) by s + 2.2376 and setting s = –2.2376
which yields
Inverting each element back to the time domain,
Although it is nice to have an analytical solution, it is generally more pleasing to engineers to view a plot of a variable as it changes with time, as shown in Figure 35. In this plot we notice that the concentration initially decreases, before increasing to a new steadystate value.
Figure 35. Response of the concentration as a function of time. See Equation ().
In Example 3.3 we solved for the coefficients (C_{1}, C_{2}, C_{3}) by selecting values of s to minimize the multiplications performed during each step. An alternative is to solve for three equations in three unknowns by using various values of s (say s = 1, 2, and 3, for example) in Equation (3.20).
Differential equations textbooks, such as Boyce and DiPrima (1992) present many examples of applications of Laplace transforms to solve differential equations. In practice, it is rare for process engineers to seek analytical solutions to differential equations; it is far easier to solve these numerically. The primary goal of this section was to provide background material to understand the transfer functions and dynamic responses shown in the next section.