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Mastering Root Locus Analysis with MATLAB: A Comprehensive Guide for Control System Students

September 30, 2023
Dr. Alexander Mitchell
Dr. Alexander Mitchell
Root Locus Plot
With a Ph.D. in Control Systems Engineering from the University of Toronto, Dr. Alexander Mitchell is a seasoned expert with extensive experience in control system analysis and MATLAB. He has assisted over 1,500 students in mastering complex control system assignments and projects.

In the realm of control systems engineering, understanding the behavior of dynamic systems is paramount. It is this understanding that forms the bedrock upon which controllers are designed to shape system responses as desired. One indispensable tool in this pursuit is the Root Locus Plot, a graphical representation of a system's poles as a function of a parameter, often the controller gain (K). In this comprehensive blog, we embark on a journey through the theoretical foundations of Root Locus Analysis, aiming to equip university students with a profound understanding of this fundamental control systems concept. This knowledge not only aids them to complete their MATLAB assignments but also empowers students to confidently navigate the complexities of control system design. We'll explore key concepts, delve into the mathematics behind them, and finally, demystify the practical implementation using MATLAB. So, let's dive into the world of Root Locus Analysis and unlock the secrets it holds for control systems engineers. If you're ready to write your root locus plot assignment using MATLAB, this guide will provide you with the essential knowledge to get started.

Navigating the Theoretical Landscape

Mastering Root Locus Analysis with MATLAB

Before we dive into the practical aspects of obtaining Root Locus Plots using MATLAB, it's essential to establish a solid theoretical foundation. This foundation will enable us to comprehend the intricacies of Root Locus Analysis and why it is a crucial tool in control systems engineering.

Throughout this comprehensive blog, we will explore the theoretical underpinnings of Root Locus Analysis, beginning with fundamental concepts such as transfer functions, poles, and zeros. We will then delve into the Root Locus technique itself, elucidating its key principles, including the Open-Loop Transfer Function (OLTF) and the Pole-Zero Plot. Understanding these concepts is essential as they form the bedrock upon which Root Locus analysis in MATLAB is built.

MATLAB as a Powerful Tool

As we proceed, we will transition from theory to practice, demonstrating how MATLAB simplifies the process of Root Locus plotting. MATLAB, with its array of built-in functions, empowers control systems engineers and students to analyze, visualize, and design controllers effectively.

By the end of this blog, you will not only have a profound understanding of the theoretical foundations of Root Locus Analysis but also the practical skills to harness MATLAB for this purpose. Armed with this knowledge, university students will be better equipped to tackle assignments and projects related to control systems, or even better, be able to gauge which MATLAB assignment doer is best qualified to do their assignments, making this journey through the theoretical and practical aspects of Root Locus Analysis a truly enriching experience.

  1. Transfer Functions
  2. To embark on our journey into Root Locus plotting with MATLAB, it's crucial to grasp the concept of transfer functions. In control systems engineering, a transfer function G(s) is a mathematical representation of the relationship between the Laplace-transformed output Y(s)) and input U(s) signals of a linear time-invariant system:



    G(s) is the transfer function.

    Y(s) is the Laplace transform of the output signal.

    U(s) is the Laplace transform of the input signal.

    s is a complex variable.

    In essence, the transfer function G(s) encapsulates the system's dynamics and is fundamental to analyzing and designing control systems.

  3. Poles and Zeros
  4. Now, let's discuss an essential concept - poles and zeros. The poles of a transfer function are the values of s for which the denominator of the transfer function equals zero. In other words, the poles are the roots of the characteristic equation:


    Here, H(s) represents the transfer function of the controller, if one is present. The location of these poles in the complex plane is pivotal in determining the system's behavior.

  5. Root Locus Technique
  6. The Root Locus technique is a powerful tool used to analyze the movement of these poles as a parameter, often the controller gain (K), is varied. This technique provides valuable insights into the system's stability and transient response.

Key Concepts in Root Locus Analysis:

  • Open-Loop Transfer Function (OLTF): Before diving into Root Locus analysis in MATLAB, you must have the open-loop transfer function G(s)H(s)) of the system. The OLTF is the product of the plant's transfer function G(s)) and the controller's transfer function H(s)).
  • Pole-Zero Plot: A preliminary step in Root Locus analysis is to create a pole-zero plot. This plot visually represents the initial pole locations. MATLAB can generate this plot using the pzmap() function.
  • Root Locus Plot: The core of Root Locus analysis is the Root Locus plot. It depicts the possible locations of the system's poles as the controller gain (K) varies from 0 to infinity. These plots are instrumental in understanding how the system's behaviour changes under different control conditions.

Root Locus Plotting with MATLAB

Now that we've covered the foundational aspects, let's explore how MATLAB simplifies the process of obtaining Root Locus plots for a given transfer function. This step-by-step guide is invaluable for university students tackling control system assignments.

Step 1: Define the Transfer Function

In MATLAB, the first step is to define the transfer function of your system using the ‘tf()’ function. You'll need to specify the coefficients of the numerator and denominator of the transfer function. For example:

numerator = [1]; % Coefficients of the numerator

denominator = [1, 3, 2]; % Coefficients of the denominator

sys = tf(numerator, denominator); % Create the transfer function

Step 2: Generate the Root Locus Plot

To create the Root Locus Plot, MATLAB provides the rlocus() function. This function takes the system's transfer function G(s)H(s)) as input and optionally, the range of controller gains (K).

rlocus(sys); % Plot the Root Locus

Step 3: Analyze the Plot

Once the Root Locus Plot is generated, it becomes a powerful tool for analysis. Here's what to look for:

  • Stability: Determine whether the system is stable or unstable. Stable systems have all poles in the lefthalf of the complex plane.
  • Transient Response: Observe how the transient response changes as K varies. This helps you select an appropriate gain for your controller to achieve the desired response.
  • Pole Locations: Understand how the pole locations change concerning K. This insight is valuable for controller design.

Step 4: Adjust Controller Gains

Based on your analysis, you can interactively adjust the controller gain (K) in MATLAB and observe the real-time changes in the Root Locus Plot. This empowers you to make informed decisions about controller gains to achieve stability and desired transient responses in real-world control systems.


Mastering the Root Locus technique and its implementation in MATLAB is indispensable for university students studying control systems. This comprehensive guide has elucidated the theoretical underpinnings of Root Locus analysis and provided a step-by-step procedure for obtaining Root Locus Plots using MATLAB. Armed with this knowledge, students can confidently tackle assignments, analyze complex control systems, and design effective controllers to shape the behaviour of dynamic systems. Root Locus analysis is not just a tool; it's a lens through which engineers gain deep insights into the world of control systems.

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