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Simulate First & Second Order PID Closed-Loop Systems in Scilab Xcos | Tutorial

How to Simulate First and Second Order PID Closed-Loop Systems in Scilab Xcos + Editing and Exporting Figures

If you’re an engineering student or a control systems enthusiast looking for a free and powerful tool to simulate PID controllers, Scilab Xcos is an excellent choice. In this post, I’ll walk you through a step-by-step guide on how to simulate first and second order PID closed-loop systems using Scilab’s Xcos toolbox. Additionally, you’ll learn how to edit, copy, and export the simulation figures for your reports or presentations.

What is Scilab and Xcos?

Scilab is an open-source software similar to MATLAB, widely used for numerical computations. Xcos is Scilab’s graphical editor to model and simulate dynamic systems using block diagrams, making it a fantastic environment for control system simulation.

What You Will Learn in This Tutorial:

• How to set up and simulate first-order and second-order systems with PID controllers
• How to run closed-loop simulations in Xcos
• Techniques to edit simulation output figures to improve clarity and presentation
• How to copy and export figures from Scilab for external use

Step 1: Setting up the PID Controller in Xcos

Open Scilab and launch the Xcos toolbox. You’ll find blocks for PID controllers under the ‘Continuous Time’ palette.

1. Drag and drop the PID block into your workspace.
2. Connect it with your system blocks representing first or second order dynamics (like transfer function blocks).
3. Set the PID parameters (Proportional, Integral, Derivative gains) as per your control design.

Step 2: Simulating First and Second Order Systems

For a first-order system, use a simple transfer function with one pole, e.g., 1/(s+1).

For a second-order system, set up a transfer function with two poles, e.g., 1/(s^2 + 2ζω_ns + ω_n^2).

Connect these systems to your PID controller in a closed-loop configuration and run the simulation.

Step 3: Running Closed-Loop Simulation

Once everything is connected, run the simulation in Xcos. You will see output plots of your system’s response over time.

Step 4: Editing the Figures

Scilab allows you to customize your plots. You can add titles, axis labels, gridlines, and change colors or line styles to make your figures clearer.

Step 5: Copying and Exporting Figures

• Use the copy figure option to paste it into other software (like Word or PowerPoint).
• Export the figure directly as image files (.png, .jpg) or PDFs for high-quality prints.

Why Use Scilab Xcos for PID Simulation?

• Free and Open Source: No costly licenses required
• User-Friendly Graphical Interface: Easy drag-and-drop block diagram modeling
• Powerful Simulation Capabilities: Suitable for both beginners and advanced users
• Great for Control Systems Education: Perfect for academic projects and research

Final Thoughts

Simulating PID controllers using Scilab Xcos is a practical skill for anyone diving into control systems. The ability to edit and export your figures means you can easily create professional reports or presentations without additional software.

If you found this tutorial helpful, please watch the video

NPTEL GATE Website for GATE 2026 Mathematics | Free Lectures + Tests

GATE 2026 Mathematics: Free NPTEL Syllabus-Wise Lectures & Mock Tests

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- Ideal for quick self-assessment and revision

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• Note your answers and then check with the answer key.
• Focus on core topics like Electrical Machines, Power Systems, Control Systems, and Analog Electronics.
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This video description and blog content were created with the assistance of AI language models to provide clear, informative, and well-structured information for GATE 2026 Electrical Engineering aspirants.

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First-Order System PID Controller Design | Simulink Tutorial

What is a First-Order System?

Before diving into the PID controller design, it's important to understand what a first-order system is. A first-order system is a dynamic system that can be described by a first-order differential equation. It typically has one energy storage element, such as a capacitor in an electrical system or a mass in a mechanical system.

The transfer function of a first-order system is generally represented as:

G(s) = K / (τs + 1)

Where:

• K is the system gain.
• τ is the time constant.
• s is the Laplace transform variable.

Why Use PID Controllers?

A PID controller helps in adjusting the output of a system by minimizing the error between the desired setpoint and the system's actual output. It does this by using three control actions:

• Proportional (P): Responds to the current error.
• Integral (I): Addresses the accumulation of past errors.
• Derivative (D): Predicts future errors based on the rate of change.

The goal of a PID controller is to ensure that the system responds quickly to changes while avoiding overshoot and oscillations.

PI, PD, and PID Controllers: What's the Difference?

While PID is the most widely used controller, in some cases, simpler controllers like PI and PD are preferred. Each type of controller offers different benefits, depending on the system and the desired outcome.

1. Proportional-Integral (PI) Controller

Components:

• Proportional (P): Responds to the current error.
• Integral (I): Accumulates past errors over time to eliminate steady-state error.

Use Case: The PI controller is often used when a system needs to eliminate steady-state error (the difference between the desired setpoint and the actual output). It is especially useful when there's a constant or persistent error, but where the derivative action (which reacts to rate of change) isn't necessary.

Advantages:

• Helps eliminate steady-state error.
• Simpler than PID since it doesn’t use derivative action.

Disadvantages:

• May still have some overshoot or slower response compared to PID.
• Not ideal for systems with rapid changes or where derivative action is necessary.

2. Proportional-Derivative (PD) Controller

Components:

• Proportional (P): Responds to the current error.
• Derivative (D): Reacts to the rate of change of the error.

Use Case: PD controllers are used in systems where rapid response is required, and eliminating overshoot or oscillation is important. The derivative action anticipates future errors, which helps in reducing overshoot and improving system stability.

Advantages:

• Faster response and less overshoot compared to PI controllers.
• Better for systems with noise or systems requiring quick adjustments.

Disadvantages:

• Does not eliminate steady-state error.
• Sensitive to noise in the system, as derivative action amplifies high-frequency noise.

3. Proportional-Integral-Derivative (PID) Controller

Components:

• Proportional (P): Responds to the current error.
• Integral (I): Addresses the accumulation of past errors.
• Derivative (D): Predicts future errors based on the rate of change.

Use Case: PID controllers are the most common in industry due to their ability to handle both transient and steady-state errors. They are suitable for a wide range of applications where a system needs quick response times, minimal overshoot, and elimination of steady-state error.

Advantages:

• Eliminates steady-state error and reduces overshoot.
• Provides a well-rounded solution for most control systems.

Disadvantages:

• More complex than PI or PD controllers.
• Requires careful tuning of all three gains (P, I, D) to achieve optimal performance.

Step-by-Step Guide to PID Controller Design in Simulink

1. Setting Up Simulink

Start by opening MATLAB and Simulink. If you don't have Simulink installed, make sure to install it via MATLAB’s Add-Ons. Create a new model in Simulink by selecting File > New > Model.

2. Building the First-Order System Model

To model a first-order system in Simulink, follow these steps:

• Drag and drop a Transfer Function block from the Simulink > Continuous library.
• Set the transfer function to G(s) = K / (τs + 1), where K is the system gain and τ is the time constant.
• You can use a Step block to input a step signal into the system, representing the desired setpoint.

3. Designing the PID Controller

To implement the PID controller:

• From the Simulink > Continuous library, drag a PID Controller block into your model.
• Connect the output of the first-order system to the input of the PID controller.
• Adjust the PID gains (Proportional, Integral, and Derivative) based on the system's behavior. You can manually tune the PID values or use Simulink’s auto-tuning feature to optimize the gains.

4. Connecting the System

The next step is to connect the blocks:

• Connect the output of the PID controller to the input of the first-order system.
• Add a Scope block to visualize the output response and track how well the system reaches the setpoint.
• Ensure that the feedback loop is complete, where the output of the first-order system is compared with the setpoint.

5. Simulation and Tuning

Run the simulation and observe the system’s response. You'll likely need to adjust the PID controller’s parameters to achieve the desired performance. Key things to look out for:

• Overshoot: The peak that occurs when the system surpasses the desired setpoint.
• Steady-State Error: The error that remains after the system has settled.
• Settling Time: The time it takes for the system to stabilize around the setpoint.

Use the PID Tuner in Simulink to automatically optimize the controller parameters if necessary. This tool can help you fine-tune the proportional, integral, and derivative gains to minimize the error and improve performance.