Prof. Jiang is known for his contributions to stability and control of interconnected nonlinear systems, and is a key contributor to the nonlinear small-gain theory. His recent research focuses on robust adaptive dynamic programming, learning-based optimal control, nonlinear control, distributed control and optimization, and their applications to computational and systems neuroscience, connected and autonomous vehicles, and cyber-physical systems.
Prof. Jiang is a Deputy Editor-in-Chief of the IEEE/CAA Journal of Automatica Sinica and the Journal of Control and Decision, an Editor for the International Journal of Robust and Nonlinear Control and has served as Senior Editor for the IEEE Control Systems Letters (L-CSS) and Systems & Control Letters, Associate Editor and/or Guest Editor for several journals including Mathematics of Control, Signals and Systems, IEEE Transactions on Automatic Control, European Journal of Control, and Science China: Information Sciences.
Basic stability problems in networks
AI/Reinforcement learning for control
Nonlinear control theory and applications
Control of underactuated mechanical systems - for example, mobile robots, ships, underwater vehicles
Energy and power systems
Tools for cyber-physical systems
Ecole des Mines de Paris (ParisTech-Mines), France, 1993
Doctor of Philosophy, Automatic Control and Mathematics
University of Paris XI, 1989
Master of Science, Statistics
University of Wuhan, 1988
Bachelor of Science, Mathematics
New York University
From: September 2007 to present
From: September 2002 to August 2007
From: January 1999 to August 2002
Working with Professor David Hill.
From: May 1996 to May 1998
The Australian National University
Working with Prof. Iven Mareels
From: May 1994 to April 1996
The main responsibility was to solve a long-standing open problem related to the attitude control of a spacecraft with only two control inputs, and to develop novel tools and methods for underactuated mechanical systems.
From: October 1993 to April 1994
Selected Recent Papers
- Z. P. Jiang, T. Bian and W. Gao, Learning-based control: A tutorial and some recent results, Foundations and Trends in Systems and Control, Vol. 8, No. 3, pp 176–284, 2020. (Invited Paper)
- T. Bian, D. Wolpert and Z. P. Jiang, Model-free robust optimal feedback mechanisms of biological motor control, Neural Computation, 32:562-595, Mar. 2020.
- T. Bian and Z. P. Jiang, Continuous-time robust dynamic programming, SIAM J. Control and Optimization, 57 (6), pp. 4150--4174, Dec. 2019.
- W. Gao, J. Gao, K. Ozbay and Z. P. Jiang, Reinforcement-learning-based cooperative adaptive cruise control of buses in the Lincoln Tunnel corridor with time-varying topology, IEEE Transactions on Intelligent Transportation Systems, Vol. 20, No. 10, pp. 3796-3805, Oct. 2019.
- Z. P. Jiang and T. Liu, Small-gain theory for stability and control of dynamical networks: A survey, Annual Reviews in Control, Vol. 46, pp. 58-79, Oct. 2018. (Invited Paper)
- W. Gao, Z. P. Jiang and K. Ozbay, Data-driven adaptive optimal control of connected vehicles, IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 5, pp. 1122-1133, 2017.
- W. Gao and Z. P. Jiang, Nonlinear and adaptive suboptimal control of connected vehicles: a global adaptive dynamic programming approach, Journal of Intelligent & Robotic Systems, Vol. 85, pp. 597-611, 2017.
- T. Bian and Z. P. Jiang, Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design, Automatica, vol. 71, pp. 348–-360, Sept. 2016.
- Y. Jiang and Z. P. Jiang, Global adaptive dynamic programming for continuous-time nonlinear systems, IEEE Trans. Automatic Control, Vol. 60, No. 11, pp. 2917-2929, Nov. 2015.
- T. Bian, Y. Jiang and Z. P. Jiang, Decentralized adaptive optimal control of large-scale systems with application to power systems, IEEE Trans. on Industrial Electronics, Vol. 62, pp. 2439-2447, April 2015.
- T. Liu and Z. P. Jiang, A small-gain approach to robust event-triggered control of nonlinear systems, IEEE Trans. on Automatic Control, Vol. 60, No. 8, pp. 2072-2085, Aug. 2015.
- Y. Jiang and Z. P. Jiang, Adaptive dynamic programming as a theory of sensorimotor control, Biological Cybernetics, Vol. 108, pp. 459-473, 2014.
- Z. P. Jiang and T. Liu, Quantized nonlinear control - a survey, Special Issue in Celebration of the 50th Birthday of Acta Automatica Sinica, Acta Automatica Sinica, 39(11): 1820-1830, Nov. 2013. (Invited Paper)
- Z. P. Jiang and Y. Jiang, Robust adaptive dynamic programming for linear and nonlinear systems: An overview, European J. Control, Vol. 19, No. 5, pp. 417-425, 2013.
- T. Liu and Z. P. Jiang, Distributed formation control of nonholonomic mobile robots without global position measurements, Automatica, Vol. 49, pp. 592-600, 2013
- Stability and Stabilization of Nonlinear Systems. London: Springer-Verlag, June 2011. (with I. Karafyllis)
- Nonlinear Control of Dynamic Networks. CRC Press, Taylor & Francis, April 2014. (with T. Liu and D. J. Hill)
- Robust Adaptive Dynamic Programming. Wiley-IEEE Press, 2017. (with Y. Jiang)
- Nonlinear Control Under Information Constraints. Science Press, Beijing, China, 2018. (with T. Liu)
- Robust Event-Triggered Control of Nonlinear Systems. Springer Nature, June 2020. (with T. Liu and P. Zhang)
- Learning-Based Control: A Tutorial and Some Recent Results. Now Publishers, December 2020.
ISBN: 978-1-68083-752-0 (with T. Bian and W. Gao)
- Trends in Nonlinear and Adaptive Control - A tribute to Laurent Praly for his 65th birthday. Springer, London, 2021. (Edited Book with C. Prieur and A. Astolfi)
CAN Lab: My Control and Networks (CAN) Lab.mainly focuses on interdisciplinary problems at the interface of AI/machine learning and control (under nonlinear, computing and communications constraints) as well as on optimal feedback mechanisms in movement science.
Affiliated member of the Center for Advanced Technology in Telecommunications (CATT)
Affiliated member of the Connected Cites for Smart Transportation (C2SMART) Center
- Clarivate Analytics Highly Cited Researcher in 2018
- Fellow of the CAA (2017)
- Fellow of the IFAC (2013)
- Fellow of the IEEE (2008)
- Steve and Rosalind Hsia Biomedical Paper Award at 2016 WCICA.
- Best Conference Paper Award at 2015 IEEE Conf. on Information and Automation, Lijiang, China.
- Shimemura Young Author Prize (with my student Yu Jiang as the lead author) at the 2013 Asian Control Conf. in Istanbul, Turkey.
- Guan Zhao-Zhi Best Paper Award at the 2011 Chinese Control Conference.
- Best Theoretic Paper Award at the 2008 World Congress on Intelligent Control and Automation, Chongqin.
- Changjiang Chair Professorship at Beijing University (2009).
- Distinguished Overseas Chinese Scholar Award from the NSF of China (2007)
- JSPS Invitation Fellowship from the Japan Society for the Promotion of Science (2005)
- NSF CAREER Award from the National Science Foundation (2001).
- Queen Elizabeth II Research Fellowship Award from the Australian Research Council (1998).
COVID 19 has wreaked havoc across the planet. As of January 1, 2021, the WHO has reported nearly 82 million cases globally, with over 1.8 million deaths. In the face of this upheaval, public health authorities and the general population are striving to achieve a balance between safety and normalcy. The uncertainty and novelty of the current conditions call for the development of theory and simulation tools that could offer a fine resolution of multiple strata of society while supporting the evaluation of “what-if” scenarios.
The research team led by Maurizio Porfiri proposes an agent-based modeling platform to simulate the spreading of COVID-19 in small towns and cities. The platform is developed at the resolution of a single individual, and demonstrated for the city of New Rochelle, NY — one of the first outbreaks registered in the United States. The researchers used New Rochelle not only because of its place in the COVID timeline, but because agent-based modelling for mid-size towns are relatively unexplored despite the U.S. being largely composed of small towns.
Supported by expert knowledge and informed by officially reported COVID-19 data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model also accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches — in hospitals or drive-through facilities— and vaccination strategies that could prioritize vulnerable groups. Decision making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.
The study had some stark conclusions. One example: the results suggest that prioritizing vaccination of high-risk individuals has a marginal effect on the count of COVID-19 deaths. To obtain significant improvements, a very large fraction of the town population should, in fact, be vaccinated. Importantly, the benefits of the restrictive measures in place during the first wave greatly surpass those from any of these selective vaccination scenarios. Even with a vaccine available, social distancing, protective measures, and mobility restrictions will still key tools to fight COVID-19.
The research team included Zhong-Ping Jiang, professor of electrical and computer engineering; post-docs Agnieszka Truszkowska, who led the implementation of the computational framework for the project, and Brandon Behring; graduate student Jalil Hasanyan; as well as Lorenzo Zino from the University of Groningen, Sachit Butail from Southern Illinois University, Emanuele Caroppo from the Università Cattolica del Sacro Cuore, and Alessandro Rizzo from Turin Polytechnic. The work was partially supported by National Science Foundation (CMMI1561134 and CMMI-2027990), Compagnia di San Paolo, MAECI (“Mac2Mic”), the European Research Council, and the Netherlands Organisation for Scientific Research.
- Maurizio Porfiri,
- Zhong-Ping Jiang,
- Alessandro Rizzo