TEL240 Control Engineering and Automation

This course provides an in-depth exploration of control systems and feedback control, focusing on time-domain analysis and design. It begins with an introduction to control theory and the application of the Laplace transform for solving differential equations, establishing a strong foundation for dynamic system modeling in both the time and frequency domains.

Students will learn about the time response of linear time-invariant (LTI) systems, steady-state error, and stability analysis using the Routh-Hurwitz criterion, as well as step response methods. In addition, the course covers the principles and methodologies necessary for modeling, analyzing, and designing control systems. This includes the design and tuning of controllers to meet desired performance criteria in real-world systems, with a focus on Proportional-Integral-Derivative (PID) controllers, controller and observer design using state feedback, and advanced techniques such as Linear Quadratic Control (LQC) and Model Predictive Control (MPC).

Students will also study state estimation using Kalman filters, which are essential for applications like robot motion planning and control, allowing systems to predict states in the presence of uncertainties.

The course incorporates a wide range of practical examples, which are simulated, controlled, and analyzed using Python, offering students hands-on experience in applying control system concepts to real-world scenarios.

Code:

All examples and problems covered in the course are simulated using Python and can be accessed in GitHub.

By the end of the course, students will be able to:

• Model dynamic systems using both time- and frequency-domain techniques and differential equations.
• Analyze the time response of linear time-invariant (LTI) systems and assess their behavior.
• Evaluate system stability and performance using the Routh-Hurwitz stability criterion and step response methods.
• Design, implement, and tune various types of controllers, such as PID, state feedback, LQC, and MPC, and evaluate their effectiveness in meeting performance criteria.
• Understand the principles and applications of state estimation using Kalman filters and apply them to predict system states in dynamic environments with uncertainty.
• Utilize Python for control system simulation, analysis, and design, applying it to practical examples.