Our expertise in this area covers multivariable, nonlinear, optimal and model-predictive control theory, nonlinear estimation, nonlinear dynamical systems, and mechanical design. We conduct both fundamental and applied research targeting a wide range of applications including autonomous driving, factory automation and HVAC systems.
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief
Date: October 10, 2019
- MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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Where: Rensselaer Polytechnic Institute (RPI), Troy, NY
MERL Contact: Scott Bortoff
Research Areas: Control, Multi-Physical ModelingBrief
Date: September 25, 2019
- The seminar, entitled “HVAC System Control and Optimization,” was part of the Mercer Distinguished Lecture Series in the Electrical, Computer and Systems Engineering Department at Rensselaer Polytechnic Institute (RPI), Troy, NY. Given on Wednesday September 25, 2019, it focused on the systems engineering and control issues associated with highly integrated Heating, Ventilation and Air Conditioning Systems for low and zero energy buildings.
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.
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CD1377: Adaptive Optimal Control of Electrical Machines
MERL is seeking a motivated and qualified individual to conduct research in control of electrical machines. The ideal candidate should have solid backgrounds in adaptive dynamic programming and state/parameter estimation for electrical machines, demonstrated capability to publish results in leading conferences/journals, and experience with real-time control experiments involving high power devices. Senior Ph.D. students are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.
CD1392: Statistical Estimation Learning and Control of Dynamical Systems
The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct fundamental research on statistical estimation and control. The scope of the internship includes development of algorithms and property proving for estimation and control of stochastic dynamical systems. PhD students with expertise in several of sequential Monte Carlo methods, Gaussian processes, Gaussian-process state-space models, model predictive control, are welcome to apply. The candidate is expected to be proficient in Matlab, and publication of the results produced during the internship is expected. The internship duration is 3 months with flexible start date.
CD1382: Motion Planning in Dynamic Environment
MERL is seeking a highly skilled and self-motivated intern to work on motion planning of nonholonomic system in dynamic environments. The ideal candidate should have solid backgrounds in task allocation, scheduling, and motion planning under dynamic and stochastic environment. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, computer science, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.
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- "Machine Learning Methods for Predicting the Field Compressive Strength of Concrete", Construction And Building Materials, DOI: 10.1016/j.conbuildmat.2019.08.042, Vol. 228, December 2019. ,
- "Learning autonomous vehicle passengers’ preferred driving styles using g-g plots and haptic feedback", IEEE Intelligent Transportation Systems Conference (ITSC), October 2019. ,
- "Large-scale traffic control using autonomous vehicles and decentralized deep reinforcement learning", IEEE Intelligent Transportation Systems Conference (ITSC), October 2019. ,
- "On-Orbit Additive Manufacturing of Parabolic Reflector via Solar Photopolymerization", International Astronautical Congress (IAC), October 2019. ,
- "Near-optimal control of motor drives via approximate dynamic programming", IEEE International Conference on Systems, Man, and Cybernetics, October 2019. ,
- "Real-Time Optimization: A Memory-based Concurrent Extremum Seeking Approach", IFAC Nonlinear Control Systems (NOLCOS), September 2019. ,
- "Modeling and Control of Radiant, Convective, and Ventilation Systems for Multizone Residences", Building Simulation, September 2019. ,
- "Positive Invariant Sets for Safe Integrated Vehicle Motion Planning and Control", Transactions on intelligent vehicles, August 2019. ,