Model Predictive Control

[0.50] credits · Graduate

This project-based course provides a comprehensive introduction to Model Predictive Control (MPC) theory, guiding students through its development, implementation, and practical applications. The course covers system identification techniques for modeling both linear and nonlinear systems, forming the foundation for advanced MPC design. Students explore and implement various MPC algorithms, including Simplified MPC, Dynamic Matrix Control (DMC), General Predictive Control (GPC), and other conditioning methodologies. Through hands-on projects, participants gain valuable experience in designing, tuning, and applying MPC strategies to real-world control problems, equipping them with essential skills for modern control engineering.