Claus Danielson
Assistant Professor
Mechanical Engineering
University of New Mexico
PhD in Mechanical Engineering University of California, Berkeley Model Predictive Control Lab
MS in Mechanical Engineering Rensselaer Polytechnic Institute
BS in Mechanical Engineering University of Washington
Google Scholar
My research interests are in constrained planning, control, and optimization. My specialty is developing algorithms that exploit structure in large-scale and complex planning, control, and optimization problems. I have applied his research to a variety of fields include autonomous vehicles, robotics, spacecraft guidance and control, heating ventilation and air conditioning, energy storage networks, adaptive optics, atomic force microscopy, and cancer treatment.
Application Areas:
Autonomous vehicles and robotics.
My research lab develops novel motion planning algorithms for robotics and autonomous vehicles. We use optimization and invariant set theory to develop autonomous motion planning algorithms with provable guarantees on safety and robustness. Students will learn about non-convex optimization, motion planning, and machine learning.
Advanced Control of Energy Systems
My research lab develops novel control and optimization algorithms for energy systems such as photovoltaics, concentrated solar, smart grids, and heating, ventilation, and air conditioning. Controlling energy systems is challenging due to their massive scale and wide-range of timescales. We are developing scalable algorithms that ensure these systems are operated safely at peak efficiency. Students will learn dynamic system modeling, data-driven control and optimization, and efficient numerical control and optimization algorithm design.
Theoretical Areas:
Model predictive control.
Model predictive control is widely used in industry for high-performance control of systems with critical state, input, and output constraints. In model predictive control (MPC), the control input is calculated by solving an optimization problem initialized with the current state of the system. This produces an open-loop sequence of optimal control inputs, the first of which is applied to the system. Feedback is provided by repeatedly solving the optimal control with the current state of the system. MPC has many advantages: it is intrinsically formulated for multi-input/multi-output systems, it can explicitly enforce constraints, and it typically provides good closed-loop performance due to its optimization-based nature. Research in MPC requires learning about control theory, optimization, and computational geometry.
Motion Planning
Motion planning is the problem of generating a dynamically feasible collision-free trajectory between an initial state and a goal state in a generally nonconvex environment. Motion-planning is a fundamental problem in a variety of fields, for instance autonomous driving, robotics, auto-piloting of aircraft, advanced manufacturing, and spacecraft rendezvous and docking. The presence of obstacles in the environment, and hence the nonconvexity, renders this problem computationally difficult. Our research develops computationally efficient motion planning algorithms that provide rigorous guarantees on safety. Research in motion planning requires learning about control theory, computational geometry, and computer science.
Reference Governors
A reference governor is a nonlinear constrained control system that modifies the inputs (typically references) of a stable closed-loop system to ensure constraint satisfaction. A reference governor augments an existing well-designed nominal controller to provide the additional nonlinear feature of enforcing constraints. Reference governors are attractive to practitioners since they preserve the careful tuning and extensive testing of the existing controller while increase its capabilities with minimal additional computational burden. Research in reference governors requires learning about control theory and computational geometry.
Undergraduate Classes
ME-482 Robot Engineering Fall 2020
ME-380/ECE-345 Introduction to Feedback Control Spring 2020
Graduate Classes
ME-582 Robot Engineering Fall 2020
Affiliations
UNM-AFRL Agile Manufacturing Laboratory
New Mexico EPSCoR Smart Grid Center
Eric Kerrigan Profile page
- Professor of Control and Optimization Department of Electrical and Electronic Engineering - Faculty of Engineering
- 020 7594 6343 (Work)
- [email protected]
- 1114, Electrical Engineering, South Kensington Campus, United Kingdom
- Google Scholar
- ICLOCS (Imperial College London Optimal Control Software)
- Inaugural lecture
- Affiliations – Control and Power Research Group
- Affiliations – Energy Futures Lab Affiliate
- Affiliations – Space Engineering
- Affiliations – Space Lab
- Talk on co-design of optimization-based controllers
I have joint appointments in the Departments of Electrical and Electronic Engineering, and Aeronautics. I obtained a PhD in Control Engineering from the University of Cambridge and a Bachelor of Science in Electrical Engineering from the University of Cape Town. I specialise in Model Predictive Control (MPC) and its applications. MPC is the most widely adopted advanced control method across various industries due to its ability to systematically and optimally manage constraints, nonlinearities, and uncertainties. My research focuses on developing innovative numerical optimisation techniques and computer architectures that can efficiently solve the resulting optimisation problems in real-time. I am also interested in developing novel multi-objective optimisation methods for the co-design of entire closed-loop systems. By treating algorithmic, computational, and physical system parameters as design variables, substantial performance improvements can be achieved, while also reducing design time and costs. My research is driven by practical applications in aerospace, renewable energy, and information systems. This includes optimising computation and communication schedules in aerial robotic networks, reducing aerodynamic drag, and enhancing the performance of space launch and re-entry vehicles. I have supervised more than 30 PhD students and post-doctoral research associates to date, many of whom have secured tenured academic positions. I have received funding from a variety of sources, including the Engineering and Physical Sciences Research Council, the European Commission's Framework Programmes, the Royal Academy of Engineering, and the Royal Society. My research has also been supported by industrial partners such as Siemens Corporate Technology, EADS Innovation Works, The MathWorks and ESA. I have extensive consulting experience with various companies, providing expert advice on control and optimisation problems. I currently serve as an Associate Editor for the IEEE Transactions on Automatic Control. I have been a member of the IEEE Control Systems Society Board of Governors, a Senior Editor for the IEEE Transactions on Control Systems Technology and an Associate Editor for Control Engineering Practice. I have also served on the editorial boards of several other journals and conferences, including the IEEE Control Systems Society Conference Editorial Board. PHD STUDENTSHIPS AVAILABLE My departments and the university have a number of open studentships, which can be tailored to areas of mutual interest, for start dates in 2025. If you are interested in doing a PhD under my supervision in the development of novel numerical methods for predictive control and dynamic optimization, please contact me with your CV, transcript of your academic record and a personal statement. Unfortunately, because of the high number of emails that I receive, I will only reply to those who I think will stand a good chance of securing a place.
- Faculty of Engineering
FIELDS OF RESEARCH
- Electrical and Electronic Engineering
- Applied Mathematics
- Mechanical Engineering
- Numerical and Computational Mathematics
- Aerospace Engineering
- Interdisciplinary Engineering
Alberto Bemporad
Professor of Control Systems IMT School for Advanced Studies Lucca Piazza San Francesco 19, 55100 Lucca, Italy [ map ] Tel: +39 0583 4326600, Fax: +39 02 700 543345 Email: [email protected]
Research . My main research interests are in model predictive control, numerical optimization, machine learning, system identification, hybrid systems, automotive control, aerospace control, portfolio optimization, control of smart energy grids.
Publications Software Funded projects Talks Research group
My interviews at Mathworks on how to design and implement MPC controllers. My Google Scholar page . My Youtube channel
Teaching . I teach the following PhD courses:
Model Predictive Control Introduction to Machine Learning Numerical Optimization Identification, Analysis, and Control of Dynamical Systems
Undergraduate courses, short courses, and other courses/lectures
About me . Short biography
- © A. Bemporad, 2022.
- Web template: HTML5 UP
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