Related projects
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Mitacs brings innovation to more people in more places across Canada and around the world.
Learn MoreWe work closely with businesses, researchers, and governments to create new pathways to innovation.
Learn MoreNo matter the size of your budget or scope of your research, Mitacs can help you turn ideas into impact.
Learn MoreThe Mitacs Entrepreneur Awards and the Mitacs Awards celebrate inspiring entrepreneurs and innovators who are galvanizing cutting-edge research across Canada.
Learn MoreDiscover the people, the ideas, the projects, and the partnerships that are making news, and creating meaningful impact across the Canadian innovation ecosystem.
Learn MoreIn many industrial and engineering applications, process control plays a central role. Among the possible control strategies, model predictive control (MPC), also called receding horizon control (RHC), stands out for its excellent ability to handle constraints. While MPC has been successfully applied to many industrial applications, further developments are limited when it becomes necessary to solve many large on-line optimization problems. To overcome this situation, parametric optimization is often used such that most of the computation burden is pushed to an off-line phase. In this project, we propose to develop algorithms and software tools based on symbolic computation, to perform parametric optimization together with the corresponding on-line procedures, targeting MPC in the case of polynomial constraints and polynomial objective function. The proposed research will bring together the latest advances in computational real algebraic geometry and high-performance computing techniques. Our goal is to generate application driven packages for MAPLE, the flagship product of our industrial partner. In particular, the deployed infrastructure will be used to support another application relying on an off-line on-line strategy, namely code generation of parallel programs. Not only will this allow us to capitalize on the effort invested to support MPC via symbolic parametric optimization, but it will also serve as a development tool in this project by generating portable and efficient parallel code in support of our MPC solvers.
Dr. Marc Moreno Maza
Changbo Chen, Parisa Alvandi, Ning Xie & Farnam Mansouri
MapleSoft Inc.
Computer science
Information and communications technologies
Western University
Accelerate
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Find the perfect opportunity to put your academic skills and knowledge into practice!
Find ProjectsThe strong support from governments across Canada, international partners, universities, colleges, companies, and community organizations has enabled Mitacs to focus on the core idea that talent and partnerships power innovation — and innovation creates a better future.