Applied Control Systems 1: autonomous cars: Math + PID + MPC

Modeling + state space systems + PID + Model Predictive Control + Python simulation: lateral control for autonomous cars

4.54 (1727 reviews)

Udemy

platform

English

language

Engineering

What you will learn

mathematical modelling of systems

reformulating models into state-space equations

applying a PID controller to systems (simple magnetic train catching objects)

applying Model Predictive Control (MPC) to systems (autonomous car: lane changing maneuvers)

Course Gallery

Applied Control Systems 1: autonomous cars: Math + PID + MPC – Screenshot 1 — Screenshot 1 – Applied Control Systems 1: autonomous cars: Math + PID + MPC

Applied Control Systems 1: autonomous cars: Math + PID + MPC – Screenshot 2 — Screenshot 2 – Applied Control Systems 1: autonomous cars: Math + PID + MPC

Applied Control Systems 1: autonomous cars: Math + PID + MPC – Screenshot 3 — Screenshot 3 – Applied Control Systems 1: autonomous cars: Math + PID + MPC

Applied Control Systems 1: autonomous cars: Math + PID + MPC – Screenshot 4 — Screenshot 4 – Applied Control Systems 1: autonomous cars: Math + PID + MPC

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Comidoc Review

Our Verdict

Applied Control Systems 1: Autonomous Cars – Math + PID + MPC is a mathematically rigorous and highly informative course. It offers comprehensive insights into control systems, including theoretical concepts and hands-on Python simulations for lateral control in autonomous cars. Though the pace can be challenging and might require additional effort to navigate specific mathematical sections, this course provides an excellent starting point for those looking to deepen their understanding of advanced control techniques.

What We Liked

In-depth coverage of control systems with a strong focus on mathematical modeling and state-space systems.
Covers both PID and Model Predictive Control (MPC) techniques, providing a comprehensive understanding of each approach.
Well-explained physical models combined with maths create a solid foundation for course content.
Real-world examples and Python simulations enhance the learning experience.

Potential Drawbacks

Fast-paced nature and high volume of information can be overwhelming, sometimes making it hard to follow.
Navigation through mathematical details, like cost function formulation, may require extra effort.
Lack of dedicated tutorials or course materials for MATLAB/Simulink users.
Code quality could be improved for better practical application and understanding.