Dawei Shi

Postdoctoral Fellow
Paulson School of Engineering and Applied Sciences
Harvard University

Research

• My research involves the development of novel control algorithms for application to the artificial pancreas.

Education

• Ph.D., Control Systems, University of Alberta, Nov. 2014
• B.Eng., Electrical Engineering and Automation, Beijing Institute of Technology, June 2008

Contact

• Email: daweishi (at) seas (dot) harvard (dot) edu

Publications

My Google Scholar Profile

• J. Huang, D. Shi and T. Chen. “Energy-based event-triggered state estimation for hidden Markov models,” Automatica, 79, 256–264, 2017.
• W. Chen, J. Wang, D. Shi and L. Shi. “Event-based state estimation of hidden Markov models through a Gilbert-Elliott channel,” IEEE Transactions on Automatic Control, DOI: 10.1109/TAC.2017.2671037, 2017.
• D. Shi, R. J. Elliott and T. Chen. “On finite-state stochastic modeling and secure estimation of cyber-physical systems,” IEEE Transactions on Automatic Control, 62(1), pp. 65-80, 2017 (Regular Paper).
• W. Chen, D. Shi, J. Wang, and L. Shi. “Event-triggered state estimation: Experimental performance assessment and comparative study,” IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2016.2623776, 2016.
• N. He, D. Shi, M. Forbes, J. Backström and T. Chen. “Robust tuning for machine-directional predictive control of MIMO paper-making processes,” Control Engineering Practice, 55, pp. 1-12, 2016.
• D. Shi, T. Chen and M. Darouach. “Event-based state estimation of linear dynamic systems with unknown exogenous inputs,” Automatica, 69, 275-288, 2016. (Regular Paper)
• D. Shi, R. J. Elliott and T. Chen. “Event-based state estimation of discrete-state hidden Markov models,” Automatica, 65, pp. 12–26, 2016. (Regular Paper)
• J. Wu, X. Ren, D. Han, D. Shi and L. Shi. “Finite-horizon Gaussianity-preserving event-based sensor scheduling in Kalman filter applications,” Automatica, 72, 100–107, 2016.
• D. Shi, T. Chen and L. Shi. “On set-valued Kalman filtering and its application to event-based state estimation,” IEEE Transactions on Automatic Control, 60(5), pp. 1275-1290, 2015 (Regular Paper).
• Y. Cheng, D. Shi, T. Chen and Z. Shu. “Optimal data scaling for principal component pursuit: A Lyapunov approach to convergence,” IEEE Transactions on Automatic Control, 60(8), 2057-2071, 2015 (Regular Paper).
• N. He, D. Shi. ”Event-based robust sampled-data model predictive control: A non-monotonic Lyapunov function approach,” IEEE Transactions on Circuits and Systems I: Regular Papers, 62(10), pp. 2555-2564, 2015.
• D. Shi, T. Chen and L. Shi. “An event-triggered approach to state estimation with multiple point- and set-valued measurements,” Automatica, 50(6), pp. 1641-1648, 2014.
• D. Shi, T. Chen and L. Shi. “Event-triggered maximum likelihood state estimation,” Automatica, 50(1), pp. 247-254, 2014.
• D. Shi, T. Chen.”On finite-horizon l₂-induced norms of discrete-time switched linear systems,” Automatica, 49(8), pp. 2517-2524, 2013.
• D. Shi, T. Chen. “Approximate optimal periodic scheduling of multiple sensors with constraints,” Automatica, 49(4), pp. 993-1000, 2013.
• N. He, D. Shi, M. Forbes, J. Backstrom and T. Chen. ”Method and apparatus for robust tuning of model-based process controllers used with uncertain multiple-input, multiple-output (MIMO) processes,” U.S. Patent 2016/0357162, Application No. 14/729,930, Publication Date 12/08/2016.
• D. Shi, J. Wang, M. Forbes, J. Backstrom and T. Chen. “Method and apparatus for specifying and visualizing robust tuning of model-based controllers,” U.S. Patent 2015/0268645, Application No. 14/314,221, Publication Date 09/24/2015.

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