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: AI for Engineering Summer School 2019
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine LearningBrief
Date: August 19, 2019 - August 23, 2019
- Ankush Chakrabarty, a Visiting Research Scientist in MERL's Control and Dynamical Systems group, gave an invited talk at the AI for Engineering Summer School 2019 hosted by Autodesk. The talk briefly described MERL's research areas, and focused on Dr. Chakrabarty's work at MERL (with collaborators from the CD and DA group) on the use of supervised learning for verification of control systems with simulators/neural nets in the loop, and on constraint-enforcing reinforcement learning. Other speakers at the event included researchers from various academic and industrial research facilities including U Toronto, UW-Seattle, Carnegie Mellon U, the Vector Institute, and the Montreal Institute for Learning Algorithms.
MERL Contact: Stefano Di Cairano
Research Areas: Control, Dynamical Systems, Optimization, Signal ProcessingBrief
Date: June 10, 2019 - June 14, 2019
- MERL researcher Stefano Di Cairano and Prof. Ilya Kolmanovsky, Dept. Aerospace Engineering, the University of Michigan, were invited to teach a class on "Predictive and Optimization Based Control for Automotive and Aerospace Application" at the 2019 International Graduate School in Control, of the European Embedded Control Institute (EECI). Every year EECI invites world renown experts to teach 21-hours class modules, mostly for PhD students but also for professionals, on selected control subjects. Stefano and Ilya's class was attended by 30 "students" from both academia and industry, from all around the world, interested in automotive and aerospace control. The module described the fundamentals of modeling and control design in automotive and aerospace through lectures, real world examples and exercises, and placed particular emphasis on techniques such as MPC, reference governors, and optimal control.
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CD1388: Mixed-Integer Optimal Control Algorithms
MERL is looking for highly motivated individuals to work on efficient numerical algorithms and applications of mixed-integer optimal control methods. The research will involve some among the following: the study and development of mixed-integer optimization techniques for optimal control, the implementation and validation of algorithms for relevant control applications. The ideal candidate should have experience in branch-and-bound methods and presolve techniques for mixed-integer optimization and/or model predictive control. PhD students in engineering or mathematics with a focus on mixed-integer optimization or numerical optimal control are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is expected; coding parts of the algorithms in C/C++ is a big plus. The expected duration of the internship is 3-6 months and the start date is flexible.
SP1371: Object Tracking and Perception for Autonomous Driving
The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in automotive radar-based object tracking and perception for autonomous driving. Previous experience on multiple (point and extended) object tracking, data association, and data-driven object detection/tracking is highly preferred. Knowledge about automotive radar schemes (MIMO array and waveform modulation (FMCW, PMCW, and OFDM)) and hands-on experience on open automotive datasets are a plus. Knowledge on vehicle dynamics is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, conduct field measurements, data analysis (Python & MATLAB), and prepare results for patents and publication. Senior Ph.D. students with research focuses on signal processing, machine learning, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.
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.
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- "Improving LiDAR performance on a complex terrain using CFD-based correction and direct-adjoint-loop optimization", NAWEA/WindTech Conference, October 2019. ,
- "Robust Nonlinear State Estimation for a Class of Infinite-Dimensional Systems Using Reduced-Order Models", Automatica, September 2019. ,
- "Real-Time Optimization: A Memory-based Concurrent Extremum Seeking Approach", IFAC Nonlinear Control Systems (NOLCOS), September 2019. ,
- "Positive Invariant Sets for Safe Integrated Vehicle Motion Planning and Control", Transactions on intelligent vehicles, August 2019. ,
- "Online Parameter Identification for State of Power Prediction of Lithiumion Batteries in Electric Vehicles Using Extremum Seeking", International Journal of Control, Automation and Systems, August 2019. ,
- "Parameter Identification of the Nonlinear Double-Capacitor Model for Lithium-Ion Batteries: From the Wiener Perspective", American Control Conference (ACC), July 2019. ,
- "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. ,