1. Chapter 3 Modeling in the Time Domain
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Intelligent Robot Laboratory

2. Table of Contents Introduction Some Observations
General State-Space Representation
Applying State-Space Representation
Converting a Transfer Function to State Space
Converting from State Space to a Transfer Function
Linearization

3. Introduction
Tow approaches for analysis and design of feedback control systems
Transfer function approach (Classical, Frequency – domain )
This approach is based on converting a system's differential equation to a transfer function, thus generating a mathematical model of the system that algebraically relates a representation of the output to a representation of the input.
It can be applied only to linear time invariant systems or systems that can be approximated as such.
State – space approach (modern, Time - domain )
A unified method for modeling, analyzing, and designing a wide range of systems
Used to represent nonlinear systems
Handle systems with nonzero initial conditions
Represent time-varying systems, multi-input, multi-output systems
Availability of numerous state-space software packages for PCs
Not as intuitive as the classical approach for the physical interpretation
Rely on matrices and matrix operations

4. Some Observations Process of state –space approach
Select a particular subset of all possible system variables (state variables)
For an nth-order system, write n simultaneous, first-order differential equations in terms of the state variables (state equations)
If the initial conditions of all of the state variables at t0 as well as the system input for t ≥ t0 are given, we can solve the simultaneous differential equations for the state variables for t ≥ t0.
Algebraically combine the state variable with system’s input and find the other system variables for t ≥ t0 (output equation)
The state equations and the output equation  state-space representation

5. Some Observations Examples Step 1
Select a particular subset of all possible system variables => State variables
The state variables are linearly independent.
i(t), q(t)
Figure 3.2 RLC network

6. Some Observations Step 2
For an nth-order system, write n simultaneous, first-order differential equations in terms of the state variables. => State equations
(3.9)
(3.10)

7. Some Observations Step 3
If you know the initial condition of all of the state variables at t0 as well as the system input for t ≥ t0, we can solve the simultaneous differential equations for the state variables for t≥t0.

8. Some Observations Step 4
Algebraically combine the state variables with the system's input and find all of the other system variables for t ≥ t0. => Output equation
(3.13)

9. Some Observations Step 5
the state equations and the output equations => state-space representation.
(3.15)
(3.16)

10. The General State-Space Representation
Linear combination:
Linear independence: A set of variables is said to be linearly independent if none of the variables can be written as a linear combination of the others.
System variable: Any variable that responds to an input or initial conditions in a system.
State variables: The smallest set of linearly independent system variables.
State vector: A vector whose elements are the state variables.
State space: The n-dimensional space whose axes are the state variables.
(3.17)

11. The General State-Space Representation
State equations: A set of n simultaneous, fist-order differential equations with n variables, where the n variables to be solved are the state variables.
Output equation: The algebraic equation that expresses the output variables of a system as linear combinations of the state variables and the inputs.
: State equation
(3.18)
: Output equation
(3.19)

12. Applying The State-Space Representation
Must be chosen according to the following considerations:
A minimum number of state variables must be selected.
Typically, the order of the differential equation.
The number of independent energy-storage elements in system.
The components of the state vector must be linearly independent.
Linear independence
Minimum number of state variables
The minimum number = the order of the differential equation describing the system
Count the number of independent energy-storage elements in the system

13. Applying The State-Space Representation
EX 3.1 Representing an electrical network
Given the electrical network of Figure 3.5, find a state space representation if the output is the current through the resistor.
Sol) The following steps will yield a viable representation of the network in state space.
Step1. Label all of the branch currents in the network.
Figure 3.5 Electrical network for representation in state space

14. Applying The State-Space Representation
Step2. Select the state variables:
Step3. Apply network theory
Step4. State equation
Step5. Find the output equation. Since the output is iR(t),
(3.22)
(3.23)
(3.24)
(3.25)
(3.26a)
(3.26b)
(3.29a)
(3.28)
(3.29b)

15. Applying The State-Space Representation
EX 3.2 Representing an electrical network with a dependent source
Find the state and output equations for the electrical network shown in Figure 3.6 if the output vector is ,where T means transpose.
Sol) Immediately notice that this network has a voltage-dependent current source.
Step 1 Label all of the branch currents on the network
Figure 3.6 Electrical network for Example

16. Applying The State-Space Representation
Step 2 Select the state variables:
From Eq. (3.30), select the state variables to be the differentiated variables.
Step 3 Remembering that the form of the state equation is
Around the mesh containing
Node 2:
(3.30a)
(3.30b)
(3.31)
(3.32)
(3.33)
(3.34)
(3.35)

17. Applying The State-Space Representation
At Node 1 we can write the sum of the currents as
Simultaneous equations
(3.36)
(3.37a)
(3.37b)

18. Applying The State-Space Representation
Solving Eq.(3.37) simultaneously for yields
State equation
(3.38)
(3.39)
(3.40)
(3.41)

19. Applying The State-Space Representation
Step 4 Output equation
Plugging Eq. (3.38) and (3.39) into Eq. (3.42), we have
Vector matrix
(3.42a)
(3.42b)
(3.43)

20. Applying The State-Space Representation
EX. 3.3: Find the state equations for the translational mechanical system.
Sol) First write the differential equations for the network.
Figure 3.7 Translational mechanical system
(3.44)
(3.45)

21. Applying The State-Space Representation
State equation
Vector matrix form
(3.46a)
(3.46b)
(3.46c)
(3.46d)
(3.47)

22. Converting a Transfer Function to State Space
How to represent a general, nth order, linear differential equation with constant coefficients in state space in the phase – variables.
Consider the differential equation
Choose the output, y(t), and its (n-1) derivatives the state variables.
(3.48)
(3.49a)
(3.49b)
(3.49c)
(3.49d)

23. Converting a Transfer Function to State Space
Differentiating both sides yields
(3.50a)
(3.50b)
(3.50c)
(3.50d)

24. Converting a Transfer Function to State Space
Substituting the definitions of Eq.(3.49) into Eq.(3.50),
(3.51a)
(3.51b)
(3.51c)
(3.51d)

25. Converting a Transfer Function to State Space
In vector-matrix form, Eq.(3.51) becomes
(3.52)

26. Converting a Transfer Function to State Space
EX 3.4 Converting a transfer function with constant term in numerator
Figure 3.10
a. Transfer function;
b. Equivalent block diagram showing phase-variables. Note: y(t) = c(t)

27. Converting a Transfer Function to State Space
Sol) Step 1 Find the associated differential equation.
Cross-multiplying yields
Differential equation
Step Select the state variables
(3.54)
(3.55)
(3.56)
(3.57a)
(3.57b)
(3.57c)

28. Converting a Transfer Function to State Space
State and output equations
Vector matrix
(3.58a)
(3.58b)
(3.58c)
(3.58d)
(3.59a)
(3.59b)

29. Converting a Transfer Function to State Space
EX 3.5 Converting a transfer function with polynomial in numerator
Find the state space representation of the transfer function shown in Figure 3.12(a)
Sol) Step 1 Separate the system into two cascaded blocks as shown in figure 3.12(b)
Figure a. Transfer function;
Figure b. decomposed transfer function;

30. Converting a Transfer Function to State Space
Step 2 Find the state equation for the block containing the denominator.
Step 3 Introduce the effect of the block with the numerator.
Inverse Laplace transform
(3.63)
(3.64)
(3.65)
(3.66)

31. Converting a Transfer Function to State Space
Output equation. From Eq. (3.66)
Forms the output from a linear combination of the state variables as shown in Figure 3.12(c)
(3.67)
Figure 3.12
c. equivalent block diagram.

32. Converting from State Space to a Transfer Function
Given the state and output equations
Take the Laplace transform assuming zero initial conditions
Solving for X(s) in Eq.(3.69a)
(3.68a)
(3.68b)
(3.69a)
(3.69b)
(3.71)

33. Converting from State Space to a Transfer Function
Note that I is the identity matrix. Substituting Eq. (3.71) into Eq. (3.69b) yields,
Transfer Function
(3.72)
(3.73)

34. Converting from State Space to a Transfer Function
EX 3.6 State-space representation
Given the system defined by Eq. (3.74), find the transfer function, T(s)=Y(s)/U(s), where U(s) is the input and Y(s) is the output.
Sol) The solution revolves around finding the term in Eq. (3.73). All other terms are already defined. Hence, first find
(3.74a)
(3.74b)
(3.75)

35. Converting from State Space to a Transfer Function
Now form :
We obtain the final result for the transfer function
(3.76)
(3.77)

36. Linearization
If we are interested in small perturbations about an equilibrium point, we can use Taylor series.

37. Linearization EX 3.7 Representing a nonlinear system
First represent the simple pendulum shown in Figure 3.14(a) in state space: Mg is the weight, T is an applied torque in the direction, and L is the length of the pendulum. Assume the mass is evenly distributed, with the center of mass at L/2. Then linearize the state equations about the pendulum’s equilibrium point the vertical position with zero angular velocity.
Figure 3.12
a. Simple pendulum; b. Force components of Mg; c. Free-body diagram

38. Linearization
Sol) First draw a free body diagram as shown in Figure 3.14(c). Summing the torques, we get.
where J is the moment of inertia of the pendulum around the point of rotation.
Select the state variables and as phase variables.
Letting and , we write the state equations as
where is evaluated from Eq.(3.79).
Thus, we have represented a nonlinear system in state space.
It is interesting to note that the nonlinear Eq. (3.80) represents a valid and complete model of the pendulum in state space even under nonzero initial conditions and even if parameters are time varying.
(3.79)
(3.80a)
(3.80b)

39. Linearization
However, if we want to apply classical techniques and convert these state equations to a transfer function, we must linearize them.
Let us proceed now to linearize the equation point, , , that is, and
Let and be perturbed about the equilibrium point, or
Using Eq.(2.182), we obtain
From which
(3.81a)
(3.81b)
(3.82)
(3.83)

40. Linearization
Substituting Eq. (3.81) and (3.83) into (3.80) yields the following state equations.
(3.84a)
(3.84b)

41. Intelligent Robotics LaboratoryH A N K Y O U
Homework: Divide by 6
Intelligent Robotics Laboratory