Control

If it moves, we control it.

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.

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Researchers
Awards
- AWARD Arvind Raghunathan receives Roberto Tempo Best CDC Paper Award at 2022 IEEE Conference on Decision & Control (CDC)
  Date: December 8, 2022
  Awarded to: Arvind Raghunathan
  MERL Contact: Arvind Raghunathan
  Research Areas: Control, Optimization
  Brief
  - Arvind Raghunathan, Senior Principal Research Scientist in the Data Analytics group, received the IEEE Control Systems Society Roberto Tempo Best CDC Paper Award. The award was presented at the 2022 IEEE Conference on Decision & Control (CDC).
    
    The award is given annually in honor of Roberto Tempo, the 44th President of the IEEE Control Systems Society (CSS). The Tempo Award Committee selects the best paper from the previous year's CDC based on originality, potential impact on any aspect of control theory, technology, or implementation, and for the clarity of writing. This year's award committee was headed by Prof. Patrizio Colaneri, Politecnico di Milano. Arvind's paper was nominated for the award by Prof. Lorenz Biegler, Carnegie Mellon University, with supporting letters from Prof. Andreas Waechter, Northwestern University, and Prof. Victor Zavala, University of Wisconsin-Madison.
- AWARD MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019
  Date: October 10, 2019
  Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
  MERL Contact: Devesh K. Jha
  Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, Robotics
  Brief
  - 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.
See All Awards for MERL
News & Events
- NEWS MERL researchers present 7 papers at CDC 2024
  Date: December 16, 2024 - December 19, 2024
  Where: Milan, Italy
  MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; James Queeney; Abraham P. Vinod; Avishai Weiss; Gordon Wichern
  Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
  Brief
  - MERL researchers presented 7 papers at the recently concluded Conference on Decision and Control (CDC) 2024 in Milan, Italy. The papers covered a wide range of topics including safety shielding for stochastic model predictive control, reinforcement learning using expert observations, physics-constrained meta learning for positioning, variational-Bayes Kalman filtering, Bayesian measurement masks for GNSS positioning, divert-feasible lunar landing, and centering and stochastic control using constrained zonotopes.
    
    As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
    
    In addition, Ankush Chakrabarty (Principal Research Scientist, Multiphysical Systems Team) was an invited speaker in the pre-conference Workshop on "Learning Dynamics From Data" where he gave a talk on few-shot meta-learning for black-box identification using data from similar systems.
- NEWS MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
  Date: December 10, 2024 - December 15, 2024
  Where: Advances in Neural Processing Systems (NeurIPS)
  MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
  Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
  Brief
  - MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.
    
    1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530
    
    2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639
    
    3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.
    
    4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?
    
    5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.
    
    6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.
    
    7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.
    
    8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.
    
    9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.
    
    10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.
    
    11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.
    
    12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.
    
    13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.
    
    MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
See All News & Events for Control
Internships
- EA0065: Internship - Planning and Control of Mobile Manipulators
  
  MERL is seeking a highly motivated and qualified individual to conduct research in safe/robust whole-body motion planning and control of mobile manipulators. The ideal candidate should demonstrate solid background and track record of publications in the areas of robotic dynamics, motion planning, and control. Strong C++ and Python coding skills, knowledge of robotic software such as Pinocchio/Pybullet/MuJoCo, and optimization tools such as CasADi/PyTorch are a necessity. Ph.D. students in mechanical engineering, robotics, computer science, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.
  Required Specific Experience
  - Solid background and track record of conducting innovative research in the dynamic modeling, motion planning, and control of robotic systems.
  - Experience with C++/Python, Pinocchio, Pybullet, MuJoCo, CasADi, PyTorch.
- EA0074: Internship - Control Policy Learning with Guarantee
  
  MERL is seeking a highly motivated and qualified individual to conduct research in the integration of model- and learning-based control to achieve high precision positioning with guaranteed safety and robustness. The ideal candidate should have solid backgrounds in dynamical systems, control theory and state-of-the-art control policy learning algorithms, and strong coding skills. Prior experience on ultra-high precision motion control systems is a plus. Ph.D. students in learning and control are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.
- CA0107: Internship - Perception-Aware Control and Planning
  
  MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of visual perception-aware control. The overall objective is to optimize control policy where the perception uncertainty is affected by the chosen policy. Application areas include mobile robotics, drones, autonomous vehicles, and spacecraft. The ideal candidate is expected to be working towards a PhD with a strong emphasis on stochastic optimal control/planning or visual odometry and to have interest and background in as many as possible among: output-feedback optimal control, visual SLAM, POMDP, information fields, motion planning, and machine learning. The expected start date is in the late Spring/Early Summer 2025, for a duration of 3-6 months.
  Required Specific Experience
  - Current/Past enrollment in a PhD program in Mechanical, Aerospace, Electrical Engineering, or a related field
  - 2+ years of research in at least some of: optimal control, motion planning, computer vision, navigation, uncertainty quantification, stochastic planning/control
  - Strong programming skills in Python and/or C++
See All Internships for Control
Openings
- CA0093: Research Scientist - Control for Autonomous Systems
- EA0042: Research Scientist - Control & Learning
See All Openings at MERL
Recent Publications
- Vinod, A.P., Safaoui, S., Summers, T., Yoshikawa, N., Di Cairano, S., "Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2024.3433229, Vol. 32, No. 6, pp. 2492-2499, January 2025.
  BibTeX TR2024-136 PDF
  - @article{Vinod2025jan,
  - author = {Vinod, Abraham P. and Safaoui, Sleiman and Summers, Tyler and Yoshikawa, Nobuyuki and Di Cairano, Stefano}},
  - title = {Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning},
  - journal = {IEEE Transactions on Control Systems Technology},
  - year = 2025,
  - volume = 32,
  - number = 6,
  - pages = {2492--2499},
  - month = jan,
  - doi = {10.1109/TCST.2024.3433229},
  - url = {https://www.merl.com/publications/TR2024-136}
  - }
- Greiff, M., Sinhmar, H., Weiss, A., Berntorp, K., Di Cairano, S., "Invariant Set Planning for Quadrotors: Design, Analysis, Experiments", IEEE Transactions on Control Systems Technology, January 2025.
  BibTeX TR2025-010 PDF
  - @article{Greiff2025jan,
  - author = {Greiff, Marcus and Sinhmar, Himani and Weiss, Avishai and Berntorp, Karl and Di Cairano, Stefano}},
  - title = {Invariant Set Planning for Quadrotors: Design, Analysis, Experiments},
  - journal = {IEEE Transactions on Control Systems Technology},
  - year = 2025,
  - month = jan,
  - url = {https://www.merl.com/publications/TR2025-010}
  - }
- Elango, P., Vinod, A.P., Di Cairano, S., Weiss, A., "Continuous-Time Successive Convexification for Passively-Safe Six-Degree-of-Freedom Powered-Descent Guidance", AIAA SciTech, January 2025.
  BibTeX TR2025-008 PDF
  - @inproceedings{Elango2025jan,
  - author = {Elango, Purnanand and Vinod, Abraham P. and Di Cairano, Stefano and Weiss, Avishai}},
  - title = {Continuous-Time Successive Convexification for Passively-Safe Six-Degree-of-Freedom Powered-Descent Guidance},
  - booktitle = {AIAA SciTech},
  - year = 2025,
  - month = jan,
  - url = {https://www.merl.com/publications/TR2025-008}
  - }
- Lu, Z., Tu, H., Fang, H., Wang, Y., Mou, S., "Integrated Optimal Control for Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures", IEEE Transactions on Control Systems Technology, December 2024.
  BibTeX TR2025-005 PDF
  - @article{Lu2024dec,
  - author = {Lu, Zehui and Tu, Hao and Fang, Huazhen and Wang, Yebin and Mou, Shaoshuai}},
  - title = {Integrated Optimal Control for Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures},
  - journal = {IEEE Transactions on Control Systems Technology},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2025-005}
  - }
- Lu, Z., Zhang, T., Wang, Y., "Torque Constraint Modeling and Reference Shaping for Servo Systems", IEEE Control Systems Letters (L-CSS), DOI: 10.1109/LCSYS.2024.3509495, Vol. 8, pp. 2637-2642, December 2024.
  BibTeX TR2025-006 PDF
  - @article{Lu2024dec2,
  - author = {Lu, Zehui and Zhang, Tianpeng and Wang, Yebin}},
  - title = {Torque Constraint Modeling and Reference Shaping for Servo Systems},
  - journal = {IEEE Control Systems Letters (L-CSS)},
  - year = 2024,
  - volume = 8,
  - pages = {2637--2642},
  - month = dec,
  - doi = {10.1109/LCSYS.2024.3509495},
  - url = {https://www.merl.com/publications/TR2025-006}
  - }
- Zhong, J., Nikovski, D.N., Yerazunis, W.S., Ando, T., "Learning Time-Optimal Control of Gantry Cranes", International Conference on Machine Learning and Applications (ICMLA), December 2024.
  BibTeX TR2024-181 PDF
  - @inproceedings{Zhong2024dec,
  - author = {{Zhong, Junmin and Nikovski, Daniel N. and Yerazunis, William S. and Ando, Taishi}},
  - title = {Learning Time-Optimal Control of Gantry Cranes},
  - booktitle = {International Conference on Machine Learning and Applications (ICMLA)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2024-181}
  - }
- Chakrabarty, A., Deshpande, V.M., Wichern, G., Berntorp, K., "Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications", IEEE Conference on Decision and Control (CDC), December 2024.
  BibTeX TR2024-180 PDF
  - @inproceedings{Chakrabarty2024dec,
  - author = {Chakrabarty, Ankush and Deshpande, Vedang M. and Wichern, Gordon and Berntorp, Karl}},
  - title = {Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications},
  - booktitle = {IEEE Conference on Decision and Control (CDC)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2024-180}
  - }
- Greiff, M., Di Cairano, S., Berntorp, K., "Bayesian Measurement Masks for GNSS Positioning", IEEE Conference on Decision and Control (CDC), December 2024.
  BibTeX TR2024-172 PDF
  - @inproceedings{Greiff2024dec,
  - author = {Greiff, Marcus and Di Cairano, Stefano and Berntorp, Karl}},
  - title = {Bayesian Measurement Masks for GNSS Positioning},
  - booktitle = {IEEE Conference on Decision and Control (CDC)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2024-172}
  - }
See All Publications for Control
Videos

Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Robot Teams

Description of the abort-safe spacecraft rendezvous example

Positive Invariant Sets for Safe Integrated Vehicle Motion Planning and Control

Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning

[MERL Seminar Series Spring 2024] Neural Certificates and LLMs in Large-Scale Autonomy Design

[MERL Seminar Series Fall 2023] Co-Design of Complex Systems: From Autonomy to Future Mobility

[TRO 2024] Safe Multiagent Motion Planning Under Uncertainty for Drones Using Filtered Reinforcement Learning

[MERL Seminar Series Spring 2023] Learning and Dynamical Systems

[MERL Seminar Series Spring 2023] Investigating Multi-Agent Reinforcement Learning for Grid-Interactive Smart Communities using CityLearn

Robot Locomotion by Automated Controller Tuning

Real-time Mixed-integer Programming for Vehicle Decision Making and Motion Planning

Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Networks

[MERL Seminar Series Spring 2022] RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?

[MERL Seminar Series Spring 2022] Exact Structural Analysis of Multimode Modelica Models

[MERL Seminar Series 2021] Use the [Magnetic] Force for Good: Sustainability Through Magnetic Levitation

Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models

Co-simulation of HVAC Equipment and Airflow

Modelica-Based Modeling and Control of a Delta Robot

Electric Satellite Station Keeping, Attitude Control, and Momentum Management by MPC

Towards Human-Level Learning of Complex Physical Puzzles

Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving

Experimental Validation of Reachability-based Decision Making for Autonomous Driving

Particle filter-based planning demonstration using mini-cars

MPC control and particle filter-based planning demonstration using mini-cars

MPC for Satellites

HVAC Lab & Controls

MERL Research on Autonomous Vehicles

Car Path Planning

MPC for Laser Cutting

Fly Cut
Software & Data Downloads

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EA0065: Internship - Planning and Control of Mobile Manipulators

EA0074: Internship - Control Policy Learning with Guarantee

CA0107: Internship - Perception-Aware Control and Planning

CA0093: Research Scientist - Control for Autonomous Systems

EA0042: Research Scientist - Control & Learning

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