Jianqi Chen
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Robust Control Reading List

  • A course in H-infinity control theory by Bruce Francis

  • Feedback control theory by Sigurd Skogestas, Ian Postethwaite

  • Multivariable feedback control by John Doyle, Bruce Francis, and Allen Tannenbaum

  • Robust and optimal control by Kemin Zhou, John Doyle, Keith Glover

  • A course in robust control: A convex approach by Geir Dullerad and Fernando Paganini

  • Dynamics system and control by Munther Dahleh

  • Theory of robust control by Carsten Scherer

  • Linear matrix inequalities in system and control theory by Stephen Boyd, Laurent El Ghaoui, Eric Feron, and Venkataramanan Balakrishnan

Networked Control Systems

Phase Theory over Large-Scale Networks


    Complex Electrical Networks

    The small gain theorem, introduced in the 1960s, heralded a long-lasting prosperity of gain-based analysis and synthesis theory, particularly within the H-infinity framework. In contrast, the exploration of a phase-based counterpart began in the 1970s but saw limited development compared to the gain theory for many years. During this same period, energy-related concepts in engineering and physics, such as passivity and dissipativity, were integrated into control systems studies, leading to the flourishing development of dissipativity theory. Rather than acting as independent concepts, gain, phase, and dissipativity exhibit intricate yet intrinsic connections. Notably, a recent surge in phase-based research provides researchers with opportunities to reconsider the foundations of control analysis and synthesis, uncovering new connections among these essential concepts. Our study focuses on the phase properties of large-scale N-port networks, quantifying and extending the notion of passivity, which originated from the same theoretical platform.

    Representative Publication：

  • J. Chen, W. Chen, C. Chen, and L. Qiu, “Phase preservation of n-port networks under general connections” IEEE Trans. Automat. Contr.,vol. 70(4), pp. 2346-2361, 2025. (Full Paper)

  • J. Chen, W. Chen, and L. Qiu, “Phase analysis of n-port electrical networks under interconnections,” in Proc. of the 22nd IFAC World Congress, pp. 4697-4702, 2023.

  • C. Chen, W. Chen, D. Zhao, J. Chen, and L. Qiu, “A cyclic small phase theorem for MIMO LTI systems,” in Proc. of the 22nd IFAC World Congress, pp. 2033-2038, 2023.

Security and Safety over Cyber-Physical Systems


    Distributed System under Cyber Attacks

    This research investigates how to ensure the security and safety of cyber-physical systems (CPS), which tightly integrate computing, communication, and physical processes. The focus is on developing methods to detect and mitigate cyber-attacks, ensure system resilience, and maintain safe operation despite uncertainties, faults, or malicious interference. By combining control theory, network security, and real-time monitoring, the goal is to build CPS that can operate reliably and safely in adversarial or unpredictable environments, such as smart grids, autonomous vehicles, and industrial automation.

    Representative Publication：

  • J. Chen, “Unknown input filtering under full accessibility attacks” Automatica, vol. 171, pp. 111966, 2025.

  • J. Chen, J. Wei, W. Chen, H. Sandberg, K. H. Johansson, and J. Chen, “Geometrical characterization of sensor placement of cone-invariant and multi-agent systems against undetectable zero dynamics attacks,” SIAM Journal on Control and Optimization, vol. 60, pp. 890-916, 2022.

Machine Learning and Robust Control: Regret Minimization


    Regret-based Robust and Optimal Control

    Regret minimization, originating from online learning and game theory, focuses on the difference between the performance of the chosen strategy and the best possible strategy in hindsight. The goal is to minimize this regret over time. The interplay between regret minimization and robust control becomes evident in scenarios where decisions need to be made in uncertain and dynamic environments. Key points of their interplay include: 1) Focusing on minimizing regret as a robust performance metric, rather than using classical H_infty and H_2 metrics. 2) Designing online robust controllers through online learning algorithms.

    Representative Publication：

  • K. Fang, J. Chen, and J. Wu, “Clock synchronization with unknown and unmodeled disturbances over distributed networks” IEEE Trans. Contr. of Net. Syst., vol. 12(10), pp. 262-274, 2025.

Machine Learning and Robust Control: Limitation of Neural Networks in Control and Safety Verification


    Control and Safety Verification

    Using neural networks (NNs) as controllers introduces significant challenges for safety verification and stability analysis due to their inherent complexity and nonlinearity. While powerful for control tasks, rigorously verifying that an NN controller will keep a system safe (avoiding hazardous states) or stable across all possible operating conditions is computationally demanding. Methods like quadratic constraints combined with semidefinite programming (SDP) provide frameworks for this analysis by bounding the NN's behavior, but they often suffer from high computational complexity (especially for large NNs or high-dimensional systems) and can yield overly conservative results, potentially declaring safe states unsafe. Beyond those limits is our task.

    Representative Publication：

  • W. Wu, J. Chen, and J. Chen, “Stability analysis of systems with recurrent neural network controllers,” in Proc. of the 14th IFAC Workshop on Adaptive and Learning Control Systems, pp. 170-175, 2022. (Best Paper Award Finalist)

Collaborative Control of Robotic Arms: State Estimation, Imitation Learning and Beyond


    Quanser's QArm: A serial robotic manipulator

    This research focuses on enabling multiple robotic arms to work together effectively through advanced control and learning methods. It integrates state estimation to accurately track system dynamics, imitation learning to teach robots from human demonstrations or expert behaviors, and explores beyond-standard approaches such as reinforcement learning, robust control, or multi-agent coordination. The goal is to develop robust and intelligent control frameworks that allow robotic arms to collaborate safely and efficiently in dynamic and uncertain environments.

    Representative Publication：

  • X. Li, J. Chen, H. Zhang, J.Wei, and J.Wu, “Errors dynamics in affine group systems,” IEEE Trans. Automat. Contr., vol. 70(4), pp. 2607-2614, 2025.

Stability Margins Achievable by PID Control