In this course, we will study Model Predictive Control (MPC) in Robotics. MPC is a powerful and widely used framework for controlling complex robotic and dynamical systems. We begin by reviewing the mathematical foundations required for MPC, including system modeling, optimization, and constrained control formulations. This provides the necessary tools to understand how predictive control problems are formulated and solved.
We then introduce the core concept of MPC and explain how it uses a system model to predict future behavior, optimize control actions over a finite horizon, and enforce physical and safety constraints. Building on this foundation, the course will cover different classes of MPC methods, including linear MPC, nonlinear MPC, stochastic MPC, and learning-based MPC.
Throughout the course, we will emphasize both theoretical understanding and practical implementation, with examples drawn from robotics applications such as mobile robots, manipulation, and autonomous systems. By the end of the course, you will understand how MPC works, why it is effective, and how it can be applied to real-world robotic systems.
- Teacher: zhzhou, Zhou Zhehua