Learning-Based Control

Journal Papers

• K. He, S. Shi, T. van den Boom, and B. De Schutter. "State-action control barrier functions: Imposing safety on learning-based control with low online computational costs." IEEE Transactions on Automatic Control (early access), 2025. Link:  ArXiv  IEEE

• S. Shi, A. Tsiamis, and B. De Schutter. "Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control."IEEE Transactions on Automatic Control (early access), 2026. Link:  ArXiv  IEEE

• C. Liu, S. Shi, and B. De Schutter. "Certainty-Equivalence Model Predictive Control: Stability, Performance, and Beyond." Conditionally accepted by IEEE Transactions on Automatic Control, 2025.  Link:  ArXiv

• C. Liu, S. Shi, and B. De Schutter. "On the Regret of Model Predictive Control with Imperfect Inputs." IEEE Control Systems Letters, 2025. Link:  IEEE

• K. He, S. Shi, T. van den Boom, and B. De Schutter. "Approximate dynamic programming for constrained linear systems: A piecewise quadratic approximation approach." Automatica, 2024. Link: Elsevier

• K. He, S. Shi, T. van den Boom, and B. De Schutter. "Approximate Dynamic Programming for Constrained Piecewise Affine Systems with Stability and Safety Guarantees." IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024. Link:  IEEE  ArXiv

Conference Proceedings

• K. He, S. Shi, T. van den Boom, and B. De Schutter. "Efficient and Safe Learning-based Control of Piecewise Affine Systems Using Optimization-Free Safety Filters." Conference on Decision and Control (CDC), 2024. Link: IEEE

• C. Liu, S. Shi, and B. De Schutter. "Stability and performance analysis of model predictive control of uncertain linear systems." Conference on Decision and Control (CDC), 2024.  Link:  IEEE  ArXiv  

• A. Athrey, O. Mazhar, M. Guo, B. De Schutter, and S. Shi. "Regret analysis of learning-based linear quadratic gaussian control with additive exploration." European Control Conference (ECC), 2024. Link:  IEEE  ArXiv

• S. Shi and M. Lazar. “A recursively feasible distributed robust MPC algorithm for vehicle platooning.” IFAC Workshop on Distributed Estimation and Control in Networked Systems, 2018. Link: Elsevier 

• S. Shi and M. Lazar. “On distributed model predictive control for vehicle platooning with a recursive feasibility guarantee.” IFAC World Congress, 2017. Link: Elsevier 

Submitted Papers

• S. Shi, J. Sass, J. Wu, M. Kim, Y. Ma, S. Shin, R. Findeisen, R. D. Braatz,"Bang-Ride Optimal Control: Monotonicity, External Positivity, and Fast Battery Charging", submitted to IEEE Transactions on Automatic Control, 2025. Link:  ArXiv

• C. Liu, A. Anil, S. Shi, and B. De Schutter. "Robust Adaptive Discrete-Time Control Barrier Certificate." Under review by Automatica, 2025.  Link:  ArXiv

• K. He, S. Shi, T. van den Boom, and B. De Schutter. "From Learning to Safety: A Direct Data-Driven Framework for Constrained Control." Under review by IEEE Transactions on Automatic Control, 2025. Link:  ArXiv

Learning Dynamical Systems

Journal Papers

• X. Cheng, S. Shi, I. Lestas, and P. M. J. Van den Hof. “Identifiability in dynamic acyclic networks with partial excitations and measurements,” IEEE Transactions on Automatic Control, 2025. Link: IEEE  ArXiv   

• S. Shi, X. Cheng, and P. M. J. Van den Hof. “Single module identifiability in linear dynamic networks with partial excitation and measurement,” IEEE Transactions on Automatic Control, 2023. Link: IEEE  ArXiv   

• X. Cheng, S. Shi, I. Lestas, and P. M. J. Van den Hof. “A necessary condition for network identifiability with partial excitation and measurement,” IEEE Transactions on Automatic Control, 2023. Link: IEEE  ArXiv   

• S. Shi, O. Mazhar, and B. De Schutter. "Finite-sample analysis of identification of switched linear systems with arbitrary or restricted switching." IEEE Control Systems Letters, 2022. Link:  IEEE 

• S. Shi, X. Cheng, and P. M. J. Van den Hof. “Generic identifiability of subnetworks in a linear dynamic network: the full measurement case,” Automatica, 2022. Link: Elsevier  ArXiv   

• H. J. Dreef, S. Shi, X. Cheng, M. C. F. Donkers, and P. M. J. Van den Hof. “Excitation allocation for generic identifiability of linear dynamic networks with fixed modules,” IEEE Control Systems Letters, 2022. Link:  IEEE 

• X. Cheng, S. Shi, I. Lestas, and P. M. J. Van den Hof. “Allocation of excitation signals for generic identifiability of linear dynamic networks,” IEEE Transactions on Automatic Control, 2022. Link: IEEE  ArXiv   

• R. J. C. van Esch, S. Shi*, A. Bernas, S. Zinger, A. P. Aldenkamp, and P. M. J. Van den Hof. “A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect,” Computers in Biology and Medicine, 2020. *Corresponding author. Link: Elsevier  ArXiv   

Software

• Matlab toolbox for network identification (as a co-developer): https://www.sysdynet.net/ 

Associated paper: P. M. J. Van den Hof, S. Shi, et al. “SYSDYNET - A MATLAB App and Toolbox for Dynamic Network Identification.” IFAC Symposium on Systems Identification, 2024. Link: Elsevier

Conference Proceedings

• S. Shi, Z. Sun, and B. De Schutter. “A behavioral perspective on models of linear dynamical networks with manifest variables.” European Control Conference, 2024. Link: IEEE  ArXiv  

• S. Shi, X. Cheng, B. De Schutter, and P. M. J. Van den Hof. “Signal selection for local module identification in linear dynamic networks: A graphical approach.” IFAC World Congress, 2023. Link: Elsevier  

• S. Shi, X. Cheng, and P. M. J. Van den Hof. “Excitation allocation for generic identifiability of a single module in dynamic networks: A graphic approach.” IFAC World Congress, 2020. Link: Elsevier  

• X. Cheng, S. Shi, and P. M. J. Van den Hof. “Allocation of excitation signals for generic identifiability of dynamic networks.” Conference on Decision and Control, 2019. Link: IEEE  ArXiv  

• S. Shi, G. Bottegal, and P. M. J. Van den Hof. “Bayesian topology identification of linear dynamic networks.” European Control Conference, 2019. Link: IEEE  ArXiv 

Submitted Papers

• P. M. J. Van den Hof, S. Shi, S. Fonken, K. R. Ramaswamy, H. Hjalmarsson, and A. Dankers. “Data-informativity conditions for structured linear systems with implications for dynamic networks,” Under review by Automatica, 2024. Link: ArXiv