About

Zongli Lin is the Ferman W. Perry Professor in the School of Engineering and Applied Science at the University of Virginia. He serves as a Professor and Associate Chair for Graduate Studies in Electrical and Computer Engineering, and is also a Professor of Mechanical and Aerospace Engineering by courtesy. His current research interests include nonlinear control, robust control, and control applications. He has held editorial positions with the IEEE Transactions on Automatic Control, IEEE/ASME Transactions on Mechatronics, and IEEE Control Systems Magazine, and has served on the Board of Governors of the IEEE Control Systems Society. Additionally, he has chaired the IEEE Control Systems Society Technical Committee on Nonlinear Systems and Control and is involved in organizing major conferences, including serving as the general chair of the 2028 American Control Conference. He currently serves on editorial boards such as Automatica and Systems & Control Letters, and is the series editor of the Springer/Birkhauser book series Control Engineering. Zongli Lin is a Fellow of the IEEE, ASME, IFAC, and AAAS.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Mathematics
• Control engineering
• Machine Learning
• Electrical engineering
• Distributed computing
• Data Mining
• Algorithm
• Computer network
• Medicine
• Statistics

Selected publications

• Training oscillator Ising machines to assign the dynamic stability of their equilibrium points.

  PubMed · 2026-03-01

  articleOpen accessSenior author

  We propose a neural network model, which, with appropriate assignment of the stability of its equilibrium points (EPs), achieves Hopfield-like associative memory. The oscillator Ising machine (OIM), based on Kuramoto-like dynamics, is an ideal candidate for such a model, as all its 0/π binary EPs are structurally stable with their dynamic stability tunable by the coupling weights. Traditional Hopfield-based models store the desired patterns by designing the coupling weights between neurons. The design of coupling weights should simultaneously take into account both the existence and the dynamic stability of the EPs for the storage of the desired patterns. For OIMs, since all 0/π binary EPs are structurally stable, that is, any of these EPs remains an EP regardless of the values of the coupling weights or the system parameters, the design of the coupling weights needs only to focus on assigning appropriate dynamic stability for the 0/π binary EPs according to the desired patterns. In this paper, we establish a connection between the stability and the Hamiltonian energy of EPs for OIMs, and, based on this connection, provide a Hamiltonian-regularized eigenvalue contrastive method (HRECM) to train the coupling weights of OIMs for assigning appropriate stability to their EPs. Finally, numerical experiments are performed to validate the effectiveness of the proposed method.

  PublisherOA PDFDOI

• Training oscillator Ising machines to assign the dynamic stability of their equilibrium points

  Physical review. E · 2026-02-17

  articleSenior author
  PublisherDOI

• Editorial: A Special Issue in Honor of the 60th Birthday of Professor Zhong-Ping Jiang

  Unmanned Systems · 2026-04-22

  articleSenior author
  PublisherDOI

• Global Asymptotic Tracking Controller With Prescribed Performance for Uncertain Nonlinear Systems With Unknown Control Directions

  International Journal of Robust and Nonlinear Control · 2026-04-22

  articleSenior author

  ABSTRACT In this paper, we revisit the tracking problem of a nonlinear system subject to unknown time‐varying parameters, external disturbances and unknown control directions, and propose a novel adaptive controller that guarantees boundedness of all closed‐loop signals and achieves asymptotic output tracking with prescribed performance. With the help of Nussbaum functions, the control does not require the knowledge of the control directions. The introduction of an error tuning function enables global asymptotic tracking, instead of semi‐global results seen in some existing literature, and satisfaction of the prescribed performance after any prescribed, arbitrarily short, time period. The use of an asymptotic step function helps the controller to achieve asymptotic tracking as the sign function does in inducing the reaching condition in the traditional sliding mode control, while many related works only ensure the tracking error to evolve into a small neighborhood of the origin. Furthermore, viewing the unknown parameters, derivatives of the virtual control laws and higher‐order derivatives of the reference output as disturbance‐like terms to be compensated adaptively along with the disturbances, the proposed design obliviates the utilization of approximation structures, the derivatives of the virtual control laws and the higher‐order derivatives of the reference output and, thus, simplifies the design and implementation of the controller. Finally, simulation results validate the theoretical conclusions summarized above.

  PublisherDOI

• Distributed Time-Varying Optimization Over a Strongly Connected and Weight-Balanced Digraph

  2025-06-30

  articleSenior author

  This paper deals with the distributed time-varying optimization problem over a digragh (or directed graph). Motivated by the time-varying nature present in the cost functions, we model the time-varying features using an exosystem and formulate the problem of minimizing a global cost function, which is the sum of the local time-varying cost functions. We design a distributed algorithm for each agent that only utilizes the information of its own cost function and the information obtained through a network represented by a strongly connected and weight-balanced digraph. Convergence analysis is carried out to show that the decision variables of all agents converge to the time-varying optimal solution with time. Simulation results verify the theoretical conclusions.

  PublisherDOI

• Distributed secondary control of energy storage units in a droop-controlled DC microgrid

  Results in Engineering · 2025-09-11 · 1 citations

  articleOpen accessCorresponding

  In the control and management of an energy storage system consisting of multiple energy storage units, bus voltage regulation, load power sharing, and energy level balancing are important objectives. To achieve these objectives, we propose a distributed secondary control scheme for each energy storage unit in a droop-controlled multi-bus DC microgrid. This control scheme is composed of two auxiliary control inputs. By constructing a distributed voltage observer based on the dynamic average consensus algorithm of a first-order multi-agent system, we design the first auxiliary control input such that global voltage regulation is achieved. Moreover, based on the leaderless consensus algorithm of a second-order multi-agent system, we design the second auxiliary control input such that proportional power sharing and state-of-energy balancing are simultaneously achieved. Through simulation studies in MATLAB/Simulink, we validate the effectiveness of the proposed control scheme and highlight the appealing feature of state-of-energy balancing over state-of-charge balancing for a battery energy storage system. • A distributed secondary control scheme based on V-P droop control is designed. • Global voltage regulation, load power sharing and SoE balancing are all achieved. • MATLAB/Simulink simulations validate the effectiveness of the proposed control. • SoC and SoE metric differences due to the battery terminal voltage are highlighted.

  PublisherDOI

• Distributed Semiglobal Nash Equilibrium Seeking for Robotic Systems Subject to Unknown Disturbances

  IEEE Transactions on Industrial Informatics · 2025-10-10 · 1 citations

  articleSenior author

  This article investigates distributed Nash equilibrium (NE) seeking for multirobot systems with nonlinear dynamics, time-varying disturbances, and individual inequality constraints under switching communication topologies. A novel control architecture is developed by integrating adaptive radial basis function (RBF) neural networks with projection-based pseudogradient dynamics. The proposed method enables each robot to estimate and track its local NE strategy in a fully distributed manner, without requiring global information or prior knowledge of the disturbances. Unlike existing methods that rely on static graphs or known disturbance bounds, our approach ensures constraint satisfaction and disturbance rejection simultaneously under a jointly strongly connected switching network. Numerical simulations involving five 2-degrees of freedom robotic manipulators demonstrate the effectiveness of the proposed strategy, achieving convergence to the NE within 20 s, strict adherence to inequality constraints.

  PublisherDOI

• New Conditions and Controllers for State-of-Charge Balancing in Battery Energy Storage Systems

  IEEE Transactions on Automatic Control · 2025-09-01

  article

  We investigate the state-of-charge (SoC) balancing control problem for a battery energy storage system, which consists of multiple battery units. These battery units are allowed to have heterogeneous battery parameters and are connected in parallel to deliver a desired total power. Existing power allocating controllers have been developed to achieve SoC balancing without taking balancing speed into consideration. Motivated by this observation, we aim to design new power allocating controllers such that accelerated SoC balancing is achieved. To facilitate our control design, we first introduce a new concept, the powered SoC, and establish new sufficient conditions that guarantee SoC balancing among battery units in the discharging and the charging modes. Based on these new sufficient conditions, we design a power allocating controller for each battery unit. It is shown that the proposed power allocating controllers achieve accelerated SoC balancing while delivering the desired total power. One key merit of our control method lies in achieving faster SoC balancing by tuning the power in the powered SoC. Moreover, we provide in-depth discussions on the SoC evolution trends among the battery units, the limiting case, and the parameter choice. Simulation results are given to validate our analytical results.

  PublisherDOI

• Distributed Multileader Formation Tracking Within a Weight-Unbalanced Directed Network of Multiple Agents

  IEEE Transactions on Control of Network Systems · 2025-07-18 · 1 citations

  articleSenior author

  This paper investigates the problem of distributed multi-leader formation tracking of a multi-agent system within a directed network that may be weight-unbalanced. We propose two distributed formation tracking algorithms, one utilizing distributed offline estimators and the other utilizing distributed online estimators. These two types of distributed formation tracking algorithms use each agent's in-degree and out-degree information, respectively. Under the assumptions that the communication network of the follower agents is strongly connected and each leader agent is accessible by at least one follower agent, all follower agents asymptotically track the center of the leader agents with a time-varying formation offset. Simulations are performed to substantiate the theoretical conclusion.

  PublisherDOI

• Output Feedback Q-Learning for a Non-Zero-Sum Game Problem in Building HVAC Control

  Journal of Systems Science and Complexity · 2025-04-01 · 2 citations

  articleSenior author
  PublisherDOI

Recent grants

• A Truncated Prediction Approach to Control of Time-Delay Systems with Applications to High Speed Rotor/AMB Systems

  NSF · $255k · 2011–2015

• State Prediction in the Presence of Input, State and Output Delays: Application to Compressor Surge Control Using Active Magnetic Bearings

  NSF · $274k · 2015–2019

• A Generalized Absolute Stability Approach to Dealing with Saturation Nonlinearities: Analysis, Design and Applications to Magnetic Bearing Systems

  NSF · $350k · 2003–2007

Frequent coauthors

• Y. Shamash

  83 shared

• Yusheng Wei

  University of North Texas

  82 shared

• Tingshu Hu

  University of Massachusetts Lowell

  77 shared

• Ali Saberi

  Washington State University

  58 shared

• Thomas Parisini

  52 shared

• Frank L. Lewis

  51 shared

• T Echnical Activities

  New York University

  50 shared

• Lingxi Li

  Purdue University West Lafayette

  50 shared

Education

• PhD, School of Electrical Engineering and Computer Science

  Washington State University

  1994

Awards & honors

• Fellow of the Institute of Electrical and Electronics Engine…
• Fellow of the American Society of Mechanical Engineers (ASME…
• Fellow of the International Federation of Automatic Control…
• Fellow of the American Association for the Advancement of Sc…