Master Study AI

Fundamentals of Control Systems: The Backbone of Intelligent Automation

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Course Modules:

Module 1: Introduction to Control Systems

What are control systems?

Open-loop vs. closed-loop control

Examples in real-world automation and robotics

Module 2: System Modeling and Dynamics

Representing physical systems with differential equations

Transfer functions and system response

Time-domain vs. frequency-domain analysis

Module 3: Feedback and Stability

The role of feedback in control systems

Stability criteria (e.g., Routh-Hurwitz, Nyquist)

Introduction to Bode plots and root locus

Module 4: PID Controllers

Proportional, Integral, and Derivative control explained

Tuning strategies: Ziegler–Nichols, trial-and-error

Implementing PID in code for simple applications

Module 5: Control Design in Practice

Controlling temperature, position, speed, and orientation

Noise, latency, and sensor dynamics

Real-world challenges in robotic and embedded control

Module 6: Capstone Project – Design a PID-Controlled System

Simulate a control task (e.g., temperature regulation, inverted pendulum)

Model the system and apply a PID controller

Submit simulation plots, controller parameters, and analysis

Tools & Technologies Used:

Python (Control Systems Library, NumPy, Matplotlib)

MATLAB/Simulink (optional)

Arduino or microcontroller (optional for hardware demo)

Webots, PyBullet (for robotics simulation)

Target Audience:

Robotics and mechanical engineering students

AI learners working on real-time control

Developers building smart machines

Hobbyists working on DIY automation systems

Global Learning Benefits:

Understand how systems maintain stability and react to change

Design control loops for physical and simulated machines

Apply control theory to automation, mechatronics, and AI

Build a strong foundation for advanced robotics and feedback systems

 

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