Pole-zero analysis for stability in Signal Processing

Introduction

Pole-zero analysis helps us understand if a system will behave well or become unstable. It shows where the system's key points (poles and zeros) are on a graph.

Checking if an audio filter will produce clear sound without noise.

Making sure a control system in a robot moves smoothly without shaking.

Designing a signal filter that removes unwanted signals but stays stable.

Testing if an electronic circuit will work safely without oscillations.

Syntax

Signal Processing

H(z) = (z - zero1)(z - zero2)... / (z - pole1)(z - pole2)...

Poles are values of z that make the denominator zero.

Zeros are values of z that make the numerator zero.

Examples

One zero at 0.5 and one pole at 0.9.

Signal Processing

H(z) = (z - 0.5) / (z - 0.9)

Two zeros at 0.3 and -0.4, two poles at 0.8 and 0.7.

Signal Processing

H(z) = (z - 0.3)(z + 0.4) / (z - 0.8)(z - 0.7)

Sample Program

This code plots poles and zeros on the complex plane with the unit circle. It then checks if all poles are inside the unit circle to decide stability.

Signal Processing

import numpy as np
import matplotlib.pyplot as plt

# Define poles and zeros
zeros = [0.5, -0.4]
poles = [0.8, 0.7]

# Plot unit circle
theta = np.linspace(0, 2*np.pi, 400)
x = np.cos(theta)
y = np.sin(theta)

plt.figure(figsize=(6,6))
plt.plot(x, y, 'k--', label='Unit Circle')

# Plot zeros
plt.scatter(np.real(zeros), np.imag(zeros), marker='o', facecolors='none', edgecolors='b', s=100, label='Zeros')
# Plot poles
plt.scatter(np.real(poles), np.imag(poles), marker='x', color='r', s=100, label='Poles')

plt.title('Pole-Zero Plot for Stability Analysis')
plt.xlabel('Real Part')
plt.ylabel('Imaginary Part')
plt.grid(True)
plt.axis('equal')
plt.legend()
plt.show()

# Check stability: all poles must be inside unit circle
stable = all(abs(p) < 1 for p in poles)
print(f"System is {'stable' if stable else 'unstable'}.")

Important Notes

For discrete systems, poles inside the unit circle mean stability.

Zeros affect the system's frequency response but not stability directly.

Poles on or outside the unit circle mean the system can become unstable.

Summary

Pole-zero plots show key points of a system on the complex plane.

Stability depends on where poles lie relative to the unit circle.

Plotting helps visualize and decide if a system will behave well.

Practice

1. What does the location of poles inside the unit circle on a pole-zero plot indicate about a system's stability?

easy

A. The system is stable

B. The system is unstable

C. The system is marginally stable

D. The system has no poles

Solution

1. Step 1: Understand pole location and stability

   Poles inside the unit circle mean the system's response decays over time.

2. Step 2: Relate pole location to system behavior

   Since poles are inside the unit circle, the system output will not grow unbounded.

3. Final Answer:

   The system is stable -> Option A

4. Quick Check:

   Poles inside unit circle = stable system [OK]

Hint: Poles inside unit circle mean stability [OK]

Common Mistakes:

• Confusing poles inside with outside the unit circle
• Thinking zeros affect stability directly
• Ignoring the unit circle boundary

2. Which of the following is the correct Python syntax to plot poles and zeros using matplotlib for a system with poles at [0.5+0.5j, 0.5-0.5j] and zeros at [0.8]?

easy

A. plt.scatter([0.5, 0.5], [0.5, -0.5], marker='x', color='r'); plt.scatter([0.8], [0], marker='o', color='b')

B. plt.plot([0.5, 0.5], [0.5, -0.5], 'ro'); plt.plot([0.8], [0], 'bo')

C. plt.scatter([0.5, 0.5], [0.5, -0.5], marker='o', color='b'); plt.scatter([0.8], [0], marker='x', color='r')

D. plt.plot([0.5, 0.5], [0.5, -0.5], 'bx'); plt.plot([0.8], [0], 'ro')

Solution

1. Step 1: Identify correct markers for poles and zeros

   Poles are usually marked with 'x' in red, zeros with 'o' in blue.

2. Step 2: Match syntax with matplotlib scatter function

   plt.scatter([0.5, 0.5], [0.5, -0.5], marker='x', color='r'); plt.scatter([0.8], [0], marker='o', color='b') uses plt.scatter with correct markers and colors for poles and zeros.

3. Final Answer:

   plt.scatter([0.5, 0.5], [0.5, -0.5], marker='x', color='r'); plt.scatter([0.8], [0], marker='o', color='b') -> Option A

4. Quick Check:

   Correct markers and colors for poles and zeros [OK]

Hint: Poles = red 'x', zeros = blue 'o' in plots [OK]

Common Mistakes:

• Swapping markers for poles and zeros
• Using plt.plot with wrong format strings
• Plotting zeros at wrong coordinates

3. Given the poles at [0.9, 1.1] and zeros at [0.5], what will be the stability status of the system?

medium

A. Stable because one pole is inside the unit circle

B. Stable because zeros are inside the unit circle

C. Marginally stable because poles are on the unit circle

D. Unstable because one pole is outside the unit circle

Solution

1. Step 1: Check each pole's location relative to the unit circle

   One pole is at 0.9 (inside), the other at 1.1 (outside) the unit circle.

2. Step 2: Determine stability from pole locations

   Since a pole lies outside the unit circle, the system is unstable regardless of zeros.

3. Final Answer:

   Unstable because one pole is outside the unit circle -> Option D

4. Quick Check:

   Any pole outside unit circle = unstable [OK]

Hint: Any pole outside unit circle means unstable system [OK]

Common Mistakes:

• Ignoring poles outside the unit circle
• Confusing zeros with poles for stability
• Assuming zeros affect stability directly

4. The following Python code attempts to plot poles and zeros but produces an error. What is the error and how to fix it?

import matplotlib.pyplot as plt
poles = [0.7+0.7j, 0.7-0.7j]
zeros = [0.9]
plt.scatter(poles.real, poles.imag, marker='x', color='r')
plt.scatter(zeros.real, zeros.imag, marker='o', color='b')
plt.show()

medium

A. Error because marker symbols are incorrect

B. Error because poles and zeros are lists, need numpy arrays for .real and .imag

C. Error because plt.show() is missing

D. No error, code runs fine

Solution

1. Step 1: Identify data types of poles and zeros

   Poles and zeros are Python lists of complex numbers, which do not support .real or .imag attributes directly.

2. Step 2: Fix by converting lists to numpy arrays

   Convert poles and zeros to numpy arrays to access .real and .imag properties without error.

3. Final Answer:

   Error because poles and zeros are lists, need numpy arrays for .real and .imag -> Option B

4. Quick Check:

   Lists lack .real/.imag; use numpy arrays [OK]

Hint: Use numpy arrays for complex .real and .imag access [OK]

Common Mistakes:

• Trying to access .real on Python lists
• Ignoring missing plt.show()
• Using wrong marker symbols

5. You have a discrete-time system with poles at [0.6, 0.8*1j, -0.8*1j] and zeros at [0.9]. You want to improve stability by moving poles closer to the origin without changing zeros. Which pole movement will make the system more stable?

hard

A. Move poles to [0.6, 0.8j, -0.8j] (no change)

B. Move poles to [1.1, 0.9j, -0.9j]

C. Move poles to [0.4, 0.5j, -0.5j]

D. Move poles to [0.7, 0.85j, -0.85j]

Solution

1. Step 1: Understand stability and pole radius

   Poles closer to origin (smaller magnitude) mean faster decay and better stability.

2. Step 2: Compare given pole movements

   Move poles to [0.4, 0.5j, -0.5j] moves poles closer to origin (0.4 and 0.5j), reducing magnitude from original values.

3. Step 3: Confirm other options increase or keep magnitude same

   Options B and C move poles farther or slightly farther from origin, reducing stability; A is no change.

4. Final Answer:

   Move poles to [0.4, 0.5j, -0.5j] -> Option C

5. Quick Check:

   Smaller pole radius = better stability [OK]

Hint: Move poles closer to origin to improve stability [OK]

Common Mistakes:

• Moving poles outside unit circle
• Changing zeros instead of poles
• Not reducing pole magnitude