We apply dynamical systems theory in applications ranging from space probe trajectory optimization to elevator suspensions. We also develop fundamental theory and computational methods in fluid dynamics.
Where: US National Congress on Computational Mechanics 2019, in Austin Texas
MERL Contact: Mouhacine Benosman
Research Areas: Control, Data Analytics, Dynamical SystemsBrief
Date & Time: July 29, 2019; 10 AM
- MERL researcher Mouhacine Benosman will present his work on 'Learning-based Robust Stabilization for Reduced-Order Models of 3D Boussinesq Equations' as a keynote speaker at the mini-symposium 'Data assimilation in Model Order Techniques for Computational Mechanics', during the next US National Congress on Computational Mechanics 2019, in Austin Texas.
Where: Michigan State University
MERL Contacts: Scott Bortoff; Stefano Di Cairano; Abraham Goldsmith; Uroš Kalabić
Research Areas: Control, Dynamical SystemsBrief
Date: February 12, 2019
- Uros Kalabic, of MERL's Control and Dynamical Systems group, gave a talk at the Michigan State University Mechanical Engineering Seminar. The talk, entitled "Reference governors: Industrial applications and theoretical advances," covered some of the exciting research being done at MERL on reference governors and briefly described MERL's other research areas. The abstract of the talk can be found via the link below.
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CD1260: Model Predictive Control of Hybrid Systems
The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to work on hybrid model predictive control. The scope of work includes the development of model predictive control algorithms for hybrid dynamical systems, switched systems, and quantized systems, analysis and property proving, and applications in automotive, space systems, and energy systems. PhD students with expertise in some among control, optimization, model predictive control and hybrid systems, and with working knowledge of Matlab implementation are welcome to apply. The expected duration of the internship is 3-6 months with flexible start date.
MP1262: Thermal modeling for electric motors
MERL is looking for a qualified intern to conduct research on thermal modeling and temperature estimation for electric motors. The ideal candidate should have solid background in the physics and engineering of electric machines, in particular the magnetic field calculations, and loss modeling. Related experience on control and estimation theory is a plus. The candidate is expected to collaborate with MERL researchers to conduct theoretical analysis, numerical simulations, develop algorithms and prepare manuscripts for scientific publications. Senior PhD students in electrical engineering, mechanical engineering, and other related areas are encouraged to apply. The duration of the internship is about 3 months.
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- "Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze", IEEE International Conference on Robotics and Automation (ICRA), May 2019. ,
- "On Mean Field Games for Agents with Langevin Dynamics", IEEE Transactions on Control of Network Systems, DOI: 10.1109/TCNS.2019.2896975, March 2019. ,
- "Reduced-order modeling of fully turbulent buoyancy-driven flows using the Green's function method", Physical Review Fluids, DOI: 10.1103/PhysRevFluids.4.013801, Vol. 4, No. 1, December 2018. ,
- "Motion Planning of Autonomous Road Vehicles by Particle Filtering", IEEE Transactions on Intelligent Vehicles, DOI: DOI: 10.1109/TIV.2019.2904394, Vol. 4, No. 2, pp. 197-210, December 2018. ,
- "Derivative-Free Semiparametric Bayesian Models for Robot Learning", Advances in Neural Information Processing Systems (NIPS), December 2018. ,
- "On Closure Relations for Dynamic Vapor Compression Cycle Models", American Modelica Conference, October 2018. ,