Research

Multi-agent networks are broadly characterized by large collections of spatially distributed agents, which are capable of sensing, computing, communicating and actuating, and can collectively accomplish missions beyond individual capabilities. Our research is focused on developing theoretical foundations and advanced algorithms for distributed control and decision-making of multi-agent networks in dynamically changing, uncertain and adversarial environments. We are also interested in applications in robotic networks, security and the smart grid.

Below is a selected list of our current and past research topics.

  • Distributed coordination

    M. Zhu and S. Martinez. On the convergence time of asynchronous distributed quantized averaging algorithms. IEEE Transactions on Automatic Control, 56(2):386-390, 2011. PDF
    M. Zhu and S. Martinez. On discrete-time dynamic average consensus. Automatica, 46(2):322-329, 2010. PDF

    Y. Lu and M. Zhu. Distributed economic control of dynamically coupled networks. IEEE Transactions on Cybernetics, 52(3):1377-1391, 2022. PDFarxiv
    M. Zhu and E. Frazzoli. Distributed robust adaptive equilibrium computation for generalized convex games. Automatica, 63(1):82-91, 2016. PDFarxiv
    M. Zhu and S. Martinez. An approximate dual subgradient algorithm for multi-agent non-convex optimization. IEEE Transactions on Automatic Control, 58(6):1534-1539, 2013. PDFarxiv
    M. Zhu and S. Martinez. Distributed coverage games for energy-aware mobile sensor networks. SIAM Journal on Control and Optimization, 51(1):1-27, 2013. PDF
    M. Zhu and S. Martinez. On distributed convex optimization under inequality and equality constraints. IEEE Transactions on Automatic Control, 57(1):151-164, 2012. PDF arxiv
    G. Zhao and M. Zhu. Pareto optimal multi-robot motion planning. IEEE Transactions on Automatic Control, 66(9):3984-3999, 2021. PDF
    G. Zhao and M. Zhu. Scalable distributed algorithms for multi-robot near-optimal motion planning. Automatica, 140:110241, 2021. PDF
  • Cyber-physical security and privacy

    X. Zhang, Z. Yuan, S. Xu, Y. Lu and M. Zhu. Secure perception-driven control of mobile robots using chaotic encryption. 2021 American Control Conference, New Orleans, LA, pages 2575-2580, May 2021. PDF
    S. Yong, M. Zhu and E. Frazzoli. Switching and data injection attacks on stochastic cyber-physical systems: Modeling, resilient estimation and attack mitigation. ACM Transactions on Cyber-Physical Systems, Special Issue on the Internet-of-Things, 2(2):9, 2018.PDFarxiv
    P. Guo, H. Kim, N. Virani, J. Xu, M. Zhu and P. Liu. RoboADS: Anomaly detection against sensor and actuator misbehaviors in mobile robots. 2018 IEEE/IFIP International Conference on Dependable Systems and Networks, Luxembourg City, pages 574-585, June 2018. PDF
    M. Zhu and S. Martinez. On the performance analysis of resilient networked control systems under replay attacks. IEEE Transactions on Automatic Control, 59(3):804-808, 2014. PDFarxiv
    M. Zhu and S. Martinez. On attack-resilient distributed formation control in operator-vehicle networks. SIAM Journal on Control and Optimization, 52(5):3176-3202, 2014 PDF
    M. Zhu and S. Martinez. On distributed constrained formation control in operator-vehicle adversarial networks. Automatica, 49(12):3571-3582, 2013.PDF

    Y. Lu, J. Lian, M. Zhu and K. Ma. Transactive energy system deployment over insecure communication links. IEEE Transactions on Automation Science and Engineering, 2023. To appear. arxiv
    Y. Lu and M. Zhu. On privacy preserving data release of linear dynamic networks. Automatica, 115:108839, 2020. PDFarxiv
    Y. Lu and M. Zhu. A control-theoretic perspective on cyber-physical privacy: Where data privacy meets dynamic systems. Annual Reviews in Control, 47:423-440, 2019. PDF
    Y. Lu and M. Zhu. Privacy preserving distributed optimization using homomorphic encryption. Automatica, 96(10):314-325, 2018. PDFarxiv
  • Machine learning

    Z. Yuan and M. Zhu. Lightweight distributed Gaussian process regression for online machine learning. IEEE Transactions on Automatic Control, 69(6):3928-3943, 2024. arxiv
    S. Xu and M. Zhu. Online constrained meta-learning: Provable guarantees for generalization. 2023 Conference on Neural Information Processing Systems, December 2023.
    S. Xu and M. Zhu. Efficient gradient approximation method for constrained bilevel optimization. 2023 AAAI Conference on Artificial Intelligence, February 2023.
    S. Liu and M. Zhu. Distributed inverse constrained reinforcement learning for multi-agent systems. 2022 Conference on Neural Information Processing Systems, November 2022. PDF
    X. Zhang, Z. Yuan and M. Zhu. Byzantine-tolerant federated Gaussian process regression for streaming data. 2022 Conference on Neural Information Processing Systems, November 2022. PDF
    S. Saab, S. Phoha, M. Zhu and A. Ray. An adaptive polyak heavy-ball method. Machine Learning, 111(9):3245-3277, 2022. PDF
    S. Saab, K. Saab, S. Phoha, M. Zhu and A. Ray. A multivariate adaptive gradient algorithm with reduced tuning efforts. Neural Networks, 152:499-509, 2022. PDF
    Z. Hu, M. Zhu, P. Chen and P. Liu. On convergence rates of game theoretic reinforcement learning algorithms. Automatica, 104(6):90-101, 2019. PDF

    Z. Yuan and M. Zhu. dSLAP: Distributed safe learning and planning for multi-robot systems. IEEE Conference on Decision and Control, Cancun, Mexico, pages 5864-5869, December 2022. PDF
    S. Xu and M. Zhu. Meta value learning for fast policy-centric optimal motion planning. Robotics: Science and Systems, paper 61, June 2022. PDF
    Y. Lu, Y. Guo, G. Zhao and M. Zhu. Distributed safe reinforcement learning for multi-robot formation control. 2021 Mediterranean Conference on Control and Automation, Puglia, Italy, pages 1209-1214, June 2021. 

    Z. Hu, M. Zhu and P. Liu. A co-design adaptive defense scheme with bounded security damages against Heartbleed-like attacks. IEEE Transactions on Information Forensics and Security, 16:4691-4704, 2021. PDF
    Z. Hu, M. Zhu and P. Liu. Adaptive cyber defense against multi-stage attacks using learning-based POMDP. ACM Transactions on Privacy and Security, 24(1):6, 2020. PDF
    Z. Hu, P. Chen, M. Zhu and P. Liu. Reinforcement learning for adaptive cyber defense against zero-day attacks. Springer, Adversarial and Uncertain Reasoning for Adaptive Cyber Defense: Control- and Game-Theoretic Approaches to Cyber Security, S. Jajodia, G. Cybenko, P. Liu, C. Wang and M. Wellman (Eds.), 54-93, 2019. PDF
    G. Cybenko, M. Wellman, P. Liu and M. Zhu. Overview of control and game theory in adaptive cyber defenses. Springer, Adversarial and Uncertain Reasoning for Adaptive Cyber Defense: Control- and Game-Theoretic Approaches to Cyber Security, S. Jajodia, G. Cybenko, P. Liu, C. Wang and M. Wellman (Eds.), 1-11, 2019.PDF

 

  • Distributed coordination (Control, optimization, game theory and learning)
    Distributed coordination (Control, optimization, game theory and learning)

Our research is generously sponsored by: