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.
Where: 3rd IAVSD Workshop on Dynamics of Road Vehicles: Connected and Automated Vehicles
MERL Contact: Stefano Di Cairano
Research Areas: Control, Optimization, RoboticsBrief
Date: April 28, 2019
- Stefano Di Cairano, Distinguished Scientist and Senior Team Leader in the Control and Dynamical Systems Group, will give an invited talk entitled: "Modularity, integration and synergy in architectures for autonomous driving" that covers recent work in the lab concerning building a modular, robust control framework for autonomous driving.
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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- "Data-Driven Shared Steering Control of Semi-Autonomous Vehicles", IEEE Transactions on Human-Machine Systems, DOI: 10.1109/THMS.2019.2900409, May 2019. ,
- "Co-design of Safe and Efficient Networked Control Systems in Factory Automation with State-dependent Wireless Fading Channels", Automatica, Vol. 105, pp. 334-346, May 2019. ,
- "Optimal Dynamic Scheduling of Wireless Networked Control Systems", ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), May 2019, pp. 77-86. ,
- "On Mean Field Games for Agents with Langevin Dynamics", IEEE Transactions on Control of Network Systems, DOI: 10.1109/TCNS.2019.2896975, March 2019. ,
- "Data-Driven Estimation of Reachable and Invariant Sets for Unmodeled Systems via Active Learning", IEEE Conference on Decision and Control (CDC), January 2019. ,
- "Positive Invariant Sets for Safe Integrated Vehicle Motion Planning and Control", IEEE Annual Conference on Decision and Control (CDC), December 2018. ,
- "Noise-Statistics Learning of Automotive-Grade Sensors Using Adaptive Marginalized Particle Filtering", Journal of Dynamic Systems, Measurement, and Control, December 2018. ,