Nonlinear Model Predictive Control

Details

Optimization-based control explores the use of optimization algorithms for feedback control of dynamical systems. For example, model predictive control (MPC) is a widely used optimization-based control method, allowing systematic and optimal handling of constraints, nonlinearities and uncertainties.

This project will explore the design of nonlinear model predictive control (NMPC) with underlying optimization problems formulated directly based on the original problem specifications, e.g as in economic NMPC. Although such problems are typically more difficult to solve numerically, the difficulties are often offset by the availability of some guarantees in solution properties, so that any local optimum solution (to a certain extent, even any feasible solution) can be considered suitable for real-world implementation. The focus of this project is on the implementation of these solutions in a closed-loop setting, improving the robustness against various uncertainties that could arise in practice. The combination with latest development of data-driven approaches may also be included as part of the project.

Upon the successful completion of this project, the PhD candidate will gain expertise in formulating and implementing tailored NMPC control design arising from a wide range of engineering fields, including aerospace, automotive, robotics and mechatronics.