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Journal papers

  • C. Liu, S. Shi, and B. De Schutter, “On the regret of model predictive control with imperfect inputs,” IEEE Control Systems Letters, vol. 9, pp. 601-606, 2025. (online paper [open access], abstract)

  • S. Mallick, G. Battocletti, Q. Dong, A. Dabiri, and B. De Schutter, “Learning-based MPC for fuel efficient control of autonomous vehicles with discrete gear selection,” IEEE Control Systems Letters, vol. 9, pp. 1117-1122, 2025. (online paper [open access], abstract)

  • 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, 2025. To appear.

  • S. Mallick, A. Dabiri, and B. De Schutter, “Distributed model predictive control for piecewise affine systems based on switching ADMM,” IEEE Transactions on Automatic Control, vol. 70, no. 6, pp. 3727-3741, June 2025. (online paper [open access], abstract)

  • F. Airaldi, B. De Schutter, and A. Dabiri, “Reinforcement learning with model predictive control for highway ramp metering,” IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 5, pp. 5988-6004, May 2025. (online paper [open access], abstract)

  • 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, vol. 55, no. 3, pp. 1722-1734, Mar. 2025. (online paper [open access], abstract)

  • S. Mallick, F. Airaldi, A. Dabiri, C. Sun, and B. De Schutter, “Reinforcement learning-based model predictive control for greenhouse climate control,” Smart Agricultural Technology, vol. 10, p. 100751, Mar. 2025. (online paper [open access], abstract)

  • L. Gharavi, C. Liu, B. De Schutter, and S. Baldi, “Sensitivity analysis for piecewise-affine approximations of nonlinear programs with polytopic constraints,” IEEE Control Systems Letters, vol. 8, pp. 1271-1276, 2024. (online paper, abstract)

  • A. Jamshidnejad and B. De Schutter, “A combined probabilistic-fuzzy approach for dynamic modeling of traffic in smart cities: Handling imprecise and uncertain traffic data,” Computers and Electrical Engineering, vol. 119-A, p. 109552, 2024. (online paper [open access], abstract)

  • X. Liu, A. Dabiri, Y. Wang, J. Xun, and B. De Schutter, “Distributed model predictive control for virtually coupled heterogeneous trains: Comparison and assessment,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, pp. 20753-20766, 2024. (online paper [open access], abstract)

  • D. Sun, A. Jamshidnejad, and B. De Schutter, “A novel framework combining MPC and deep reinforcement learning with application to freeway traffic control,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 6756-6769, 2024. (online paper [open access], abstract)

  • D. Sun, A. Jamshidnejad, and B. De Schutter, “Adaptive parameterized model predictive control based on reinforcement learning: A synthesis framework,” Engineering Applications of Artificial Intelligence, vol. 136-B, p. 109009, Oct. 2024. (online paper [open access], abstract)

  • S. Mallick, F. Airaldi, A. Dabiri, and B. De Schutter, “Multi-agent reinforcement learning via distributed MPC as a function approximator,” Automatica, vol. 167, p. 111803, Sept. 2024. (online paper [open access], abstract)

  • X. Liu, A. Dabiri, Y. Wang, and B. De Schutter, “Real-time train scheduling with uncertain passenger flows: A scenario-based distributed model predictive control approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 5, pp. 4219-4232, May 2024. (online paper [open access], abstract)

  • 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, vol. 160, p. 111456, Feb. 2024. (online paper [open access], abstract)

  • 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, vol. 7, pp. 121-126, 2023. (online paper, abstract)

  • X. Liu, A. Dabiri, J. Xun, and B. De Schutter, “Bi-level model predictive control for metro networks: Integration of timetables, passenger flows, and train speed profiles,” Transportation Research Part E, vol. 180, p. 103339, Dec. 2023. (online paper [open access], abstract)

  • A. Jamshidnejad, D. Sun, A. Ferrara, and B. De Schutter, “A novel bi-level temporally-distributed MPC approach: An application to green urban mobility,” Transportation Research Part C, vol. 156, p. 104334, Nov. 2023. (online paper [open access], abstract)

  • J. Jeschke, D. Sun, A. Jamshidnejad, and B. De Schutter, “Grammatical-evolution-based parameterized model predictive control for urban traffic networks,” Control Engineering Practice, vol. 132, p. 105431, Mar. 2023. (online paper [open access], abstract)

  • X. Liu, A. Dabiri, Y. Wang, and B. De Schutter, “Modeling and efficient passenger-oriented control for urban rail transit networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 3325-3338, Mar. 2023. (online paper [open access], abstract)

  • A. Ilioudi, A. Dabiri, B.J. Wolf, and B. De Schutter, “Deep learning for object detection and segmentation in videos: Towards an integration with domain knowledge,” IEEE Access, vol. 10, pp. 34562-34576, 2022. (online paper [open access], abstract)