Exploring Symbolic Techniques for Fast Robust Nonlinear Model Predictive Control of Autonomous Vehicles
The goal of this project is to design computationally-efficient solvers that can be used for autonomous vehicle control developments. Because autonomous cars have complex mathematical models, it is usually hard to perform their necessary control computations on-line and when the vehicle is running. Therefore, it is required to come up with much faster solvers for their controllers. At the end of this project, the developed control methods will be tested on an accurate simulation platform to evaluate their performance and robustness in realistic scenarios. The partner organization will add the developed method to their resources to improve its capabilities. Moreover, Intern will generate webinars and whitepapers that the organization partner will use for marketing purposes.