Model Predictive Control: Theory and Practical Applications

In modern industries, from chemical processing to robotics, maintaining precise control over complex systems is a major challenge. Model Predictive Control (MPC) provides a powerful framework to handle multivariable systems and constraints effectively. This text-based course guides you from the fundamental mathematical concepts of MPC to practical implementation strategies. You will understand how to model system dynamics, formulate optimization problems, and handle real-world constraints to design robust controllers. What you'll learn: - Understand the core principles of predictive control, state-space representations, and system dynamics - Formulate objective functions and constraints for linear and non-linear systems - Apply quadratic programming and modern optimization techniques to solve control problems - Integrate state estimation using Kalman filters to handle noisy and incomplete measurements - Simulate control loops and analyze closed-loop stability and performance - Design practical MPC controllers for real-world industrial scenarios The course begins with foundational control theory and mathematical modeling before moving into optimization algorithms, constraint handling, and practical simulation techniques. It is designed for engineering students, automation enthusiasts, and professionals new to advanced control systems, requiring no prior experience with MPC. Start reading today to master the core principles of model predictive control and elevate your engineering skills.