Publications

2024

(12) K. Yates, A.J. Berliner, G. Makrygiorgos, et al. Nitrogen accountancy in space agriculture. npj Microgravity, 10, 90, 2024. LINK

(11) K. Shao, K. Cho, and A. Mesbah. Time-varying Bayesian optimization for MPC calibration for run-to-run drifting systems: A study on discrete-temporal kernels. IFAC-PapersOnLine, 58 (18), 214-219, 2024. LINK

(10) S. Hirt, M. Pfefferkorn, A. Mesbah, and R. Findeisen. Stability-informed Bayesian optimization for MPC cost function learning. IFAC-PapersOnLine, 58 (18), 208-213, 2024. LINK

(9) A. Kudva, M.T. Huynh, A. Mesbah, and J.A. Paulson. Efficient Performance-based MPC Tuning in High Dimensions using Bayesian Optimization over Sparse Subspaces. In Proceedings of the 12th IFAC International Symposium on Advanced Control of Chemical Processes, 2024, Canada. LINK

(8) K.J. Chan, J.A. Paulson, and A. Mesbah. A practical multiobjective learning framework for optimal hardware-software co-design of control-on-a-chip systems, IEEE Transactions on Control Systems Technology, 32 (6), 2178-2193, 2024. LINK

(7) G. S. Oehrlein, et al. Future of plasma etching for microelectronics: Challenges and opportunities. Journal of Vacuum Science & Technology B, 42, 041501, 2024. LINK

(6) K. Cho, K. Shao, A. Mesbah. Run-indexed time-varying Bayesian optimization with positional encoding for auto-tuning of controllers: Application to a plasma-assisted deposition process with run-to-run drifts. Computers & Chemical Engineering, 185, 108653, 2024. LINK

(5) V. Miller, D. Clark, A. Mesbah. Ammonia retention in biowaste via low-temperature-plasma-synthesized nitrogen oxyacids: Toward sustainable upcycling of animal waste. ACS Sustainable Chemistry & Engineering, 12, 2621-2631, 2024. LINK

(4) K. Sawlani, A. Mesbah. Chapter 4 - Perspectives on artificial intelligence for plasma-assisted manufacturing in semiconductor industry. Artificial Intelligence in Manufacturing, M. Soroush, R. Braatz, Academic Press, 97-138, 2024. LINK

(3) K. Shao, A. Mesbah. A study on the role of electric field in low-temperature plasma catalytic ammonia synthesis via integrated density functional theory and microkinetic modeling. JACS Au, 4, 525-544, 2024. LINK

(2) A.D. Bonzanini, A. Mesbah, and S. Di Cairano. Perception-aware model predictive control for constrained control in unknown environments, Automatica, 160, 111418, 2024. LINK

(1) P. Daoutidis et al. Machine leaning in process systems engineering: Challenges and opportunities, Computers & Chemical Engineering, 181, 108523, 2024. LINK

2023

(20) K. Shao, D. Romeres, A. Chakrabarty, A. Mesbah. Preference-guided Bayesian optimization for control policy learning: Application to personalized plasma medicine. Advances in Neural Information Processing Systems 2023, TR2023-146, 2023. LINK

(19) K.J. Chan, J.A. Paulson, and A. Mesbah. Safe explorative Bayesian optimization – Towards personalized treatments in plasma medicine. In Proceedings of the 62nd IEEE Conference on Decision and Control, 4106-4111, 2023, Singapore. LINK

(18) G. Makrygiorgos, J.A. Paulson, and A. Mesbah. No-regret Bayesian optimization with gradients using local optimality-based constraints: Application to closed-loop policy search. In Proceedings of the 62nd IEEE Conference on Decision and Control, 20-25, 2023, Singapore. LINK

(17) I. Nodozi, C. Yan, M. Khare, A. Halder, and A. Mesbah. Neural Schrödinger bridge with Sinkhorn losses: Application to data-driven minimum effort control of colloidal self-assembly, IEEE Transactions on Control Systems Technology, 32 (3), 960-973, 2023. LINK

(16) G. Makrygiorgos, G.M. Maggioni, and A. Mesbah. A physics-assisted data driven framework for predicting crystal chord length distributions under arbitrary experimental conditions, Organic Process Research and Development, 10.1021, 2023. LINK

(15) L. Afsah-Hejri, P. Rajaram, J. O’Leary, J. McGivern, R. Baxter, A. Mesbah, R. Maboudian, R. Ehsani. Identification of volatile organic compounds (VOCs) by SPME-GC-MS to detect Aspergillus flavus infection in pistachios, Food Control, 154, 110033, 2023. LINK

(14) R. Anirudh, et al. 2022 Review of data-driven plasma science. IEEE Transactions on Plasma Science, 1-89, 2023. LINK

(13) K.T. Hoang, S. Boersma, A. Mesbah, and L. Imsland. Heteroscedastic Bayesian optimisation for active power control of wind farms. In Proceedings of the 22nd IFAC World Congress, 8289-8294, 2023, Yokohama. LINK

(12) D. Rodrigues and A. Mesbah. Adaptive global solutions to single-input optimal control problems via Gaussian processes. In Proceedings of the 22nd IFAC World Congress, 5275-5280, 2023, Yokohama. LINK

(11) G. Makrygiorgos*, J.A. Paulson*, and A. Mesbah. Gradient-enhanced Bayesian optimization via acquisition ensembles with application to reinforcement learning. In Proceedings of the 22nd IFAC World Congress, 698-703, 2023, Yokohama. LINK [*equal contribution]

(10) R. Soloperto, A. Mesbah, and F. Allgöwer. Safe exploration and escape local minima with model predictive control under partially unknown constraints. IEEE Transactions on Automatic Control, 68 (12), 7530-7545, 2023. LINK

(9) J.A. Paulson, F. Sorourifar, and A. Mesbah. A tutorial on derivative-free policy learning methods for interpretable controller representations. In Proceedings of the American Control Conference, 1295-1306, 2023, San Diego. LINK

(8) P.K. Shahri, B. HomChaudhuri, S.S. Pulugurtha, A. Mesbah, and A.H. Ghasemi. Traffic congestion control using distributed extremum seeking and filtered feedback linearization control approaches. In Proceedings of the American Control Conference, 1535-1540, 2023, San Diego. LINK

(7) I. Nodozi, J. O’Leary, A. Mesbah, and A. Halder. A physics-informed deep learning approach for minimum effort stochastic control of self-assembly. In Proceedings of the American Control Conference, 609-615, 2023, San Diego. LINK

(6) K.J. Chan*, G. Makrygiorgos*, and A. Mesbah. Towards personalized plasma medicine via data-efficient adaptation of fast deep learning -based MPC policies. In Proceedings of the American Control Conference, 2769-2775, 2023, San Diego. LINK [*co-first authors]

(5) J. O’Leary, M.M. Khare, and A. Mesbah. Novelty search for neuroevolutionary reinforcement learning of deceptive systems: An application to control of colloidal self-assembly. In Proceedings of the American Control Conference, 2776-2781, 2023, San Diego. LINK

(4) A.D. Bonzanini, K. Shao, D.B. Graves, S. Hamaguchi, and A. Mesbah. Foundations of machine learning for low-temperature plasmas: Methods and case studies, Plasma Sources Science and Technology, 32 (2), 024003, 2023. LINK

(3) Y. Bao, K.J. Chan, A. Mesbah, and J. Mohammadpour Velni. Learning-based adaptive-scenario-tree model predictive control with improved probabilistic safety using robust Bayesian neural networks, International Journal of Robust and Nonlinear Control, 33 (5), 3312-3333, 2023. LINK

(2) G. Makrygiorgos, A.J. Berliner, F. Shi, D.S. Clark, A.P. Arkin, and A. Mesbah. Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems, Biotechnology and Bioengineering, 120 (7), 803-818, 2023. LINK

(1) D. Rodrigues, K.J. Chan, and A. Mesbah. Data-driven adaptive optimal control under model uncertainty: An application to cold atmospheric plasmas, IEEE Transactions on Control Systems Technology, 31 (1), 55-69, 2023. LINK

2022

(16) P.K. Shahri, B. HomChaudhuri, S.S. Pulugurtha, A. Mesbah, and A.H. Ghasemi. Traffic Congestion Control using Distributed Extremum Seeking and Filtered Feedback Linearization Control Approaches, IEEE Control Systems Letters, 7, 1003-1008, 2022. LINK

(15) A.D. Bonzanini, J.A. Paulson, G. Makrygiorgos, and A. Mesbah. Scalable Estimation of Invariant Sets for Mixed-Integer Nonlinear Systems using Active Deep Learning. In Proceedings of the 61st IEEE Conference on Decision and Control, 3431-3437, 2022, Cancún, Mexico. LINK

(14) J. O’Leary, J.A. Paulson, and A. Mesbah. Stochastic physics-informed neural ordinary differential equations. Journal of Computational Physics, 468, 111466, 2022. LINK

(13) R. Mao*, J. O’Leary*, A. Mesbah, and J. Mittal. A deep learning framework discovers compositional order and self-assembly pathways in binary colloidal mixtures. JACS Au, 2 (8), 1818-1828, 2022. LINK [*co-first authors]

(12) A.J. Berliner, [and 16 others, including G. Makrygiorgos and A. Mesbah], and A.P. Arkin. Space bioprocess engineering on the horizon. Communications Engineering, 1, 13, 2022. LINK

(11) A. Mesbah, K.P. Wabersich, A.P. Schoellig, M.N. Zeilinger, S. Lucia, T.A. Badgwell, and J.A. Paulson. Fusion of machine learning and MPC under uncertainty: What advances are on the horizon?. In Proceedings of the American Control Conference, 342-357, 2022, Atlanta. LINK

(10) Y. Bao, K.J. Chan, A. Mesbah, and J. Mohammadpour Velni. Learning-based adaptive-scenario-tree model predictive control with probabilistic safety guarantees using Bayesian neural networks. In Proceedings of the American Control Conference, 3260-3265, 2022, Atlanta. LINK

(9) A.D. Bonzanini, A. Mesbah, and S. Di Cairano. Multi-stage perception-aware chance-constrained MPC with applications to automated driving. In Proceedings of the American Control Conference, 1697-1702, 2022, Atlanta. LINK

(8) D. Rodrigues, G. Makrygiorgos, and A. Mesbah. Tractable global solutions to chance-constrained Bayesian optimal experiment design for arbitrary prior and noise distributions, Journal of Process Control, 116, 1-18, 2022. LINK

(7) K. Shao, X. Pei, D.B. Graves, and A. Mesbah. Active learning-guided exploration of parameter space of air plasmas to enhance the energy efficiency of NOx production, Plasma Sources Science and Technology, 31, 055018, 2022. LINK

(6) G. Makrygiorgos, A.D. Bonzanini, V. Miller, and A. Mesbah. Performance-oriented model learning for control via multi-objective Bayesian optimization, Computers and Chemical Engineering, 162, 107770, 2022. LINK

(5) D. Rodrigues and A. Mesbah. Efficient Global Solutions to Single-Input Optimal Control Problems via Approximation by Sum-of-Squares Polynomials, IEEE Transactions on Automatic Control, 67 (9), 4674-4686, 2022. LINK

(4) J.A. Paulson, G. Makrygiorgos, and A. Mesbah. Adversarially robust Bayesian optimization for efficient auto-tuning of generic control structures under uncertainty, AIChE Journal, 68 (6), e17591, 2022. LINK

(3) A.D. Bonzanini, D.B. Graves, and A. Mesbah. Learning-based SMPC for reference tracking under state-dependent uncertainty: An application to atmospheric pressure plasma jets for plasma medicine, IEEE Transactions on Control Systems Technology, 30 (2), 611-624, 2022. LINK

(2) M. Laroussi, [and 19 others including A. Mesbah], Low Temperature Plasma for Biology, Hygiene, and Medicine: Perspective and Roadmap, IEEE Transactions on Radiation and Plasma Medical Sciences, 6 (2), 127-157, 2022. LINK

(1) A.D. Bonzanini, K. Shao, A. Stancampiano, D.B. Graves, and A. Mesbah. Perspectives on machine learning-assisted plasma medicine: Towards automated plasma treatment, IEEE Transactions on Radiation and Plasma Medical Sciences, 6 (1), 16-32, 2022. LINK

2021

(13) D. Rodrigues and A. Mesbah. Multivariable control based on incomplete models via feedback linearization and continuous-time derivative estimation, International Journal of Robust and Nonlinear Control, 31 (18), 9193-9230, 2021. LINK

(12) J.A. Paulson, K. Shao, and A. Mesbah. Probabilistically Robust Bayesian Optimization for Data-Driven Design of Arbitrary Controllers with Gaussian Process Emulators. In Proceedings of the 60th IEEE Conference on Decision and Control, 3633-3639, 2021, Austin. LINK

(11) A.D. Bonzanini, A. Mesbah, and S. Di Cairano. On the Stability Properties of Perception-aware Chance-constrained MPC in Uncertain Environments. In Proceedings of the 60th IEEE Conference on Decision and Control, 2177-2182, 2021, Austin. LINK

(10) A.J. Berliner, [and 41 others, including G. Makrygiorgos and A. Mesbah], and A.P. Arkin. Towards a biomanufactory on mars, Frontiers in Astronomy and Space Sciences, 8:711550, 1-14, 2021. LINK

(9) D. Krishnamoorthy, A. Mesbah, and J.A. Paulson. An adaptive correction scheme for offset-free asymptotic performance in deep learning-based economic MPC. In Proceedings of the IFAC Symposium on Advanced Control of Chemical Processes, 1-6, 2021, Virtual. LINK

(8) F. Sorourifar, G. Makrygiorgos, A. Mesbah, and J.A. Paulson. A data-driven automatic tuning method for MPC under uncertainty using constrained Bayesian optimization. In Proceedings of the IFAC Symposium on Advanced Control of Chemical Processes, 1-8, 2021, Virtual. LINK

(7) A.D. Bonzanini, A. Mesbah, and S. Di Cairano. Perception-aware chance-constrained model predictive control for uncertain environments. In Proceedings of the American Control Conference, 2078-2083, 2021, New Orleans. LINK

(6) K.J. Chan, J.A. Paulson, and A. Mesbah. Deep learning-based approximate nonlinear model predictive control with offset-free tracking for embedded applications. In Proceedings of the American Control Conference, 3466-3472, 2021, New Orleans. LINK

(5) L.Z. Benet, J.K. Sodhi, G. Makrygiorgos, and A. Mesbah. There is only one valid definition of clearance: Critical examination of clearance concepts reveals the potential for errors in clinical drug dosing decisions, AAPS J, 23 (67), 2021. LINK

(4) J. O’Leary, R. Mao, E.J. Pretti, J.A. Paulson, J. Mittal, and A. Mesbah. Deep Learning for characterizing the self-assembly of three-dimensional colloidal systems, Soft Matter, 17, 989-999, 2021. LINK

(3) D. Gidon, H.S. Abbas, A.D. Bonzanini, D.B. Graves, J. Mohammadpour Velni, and A. Mesbah. Data-driven LPV model predictive control of a cold atmospheric plasma jet for biomaterials processing, Control Engineering Practice, 109, 104725, 2021. LINK

(2) A.D. Bonzanini, J.A. Paulson, G. Makrygiorgos, and A. Mesbah. Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks, Computers and Chemical Engineering, 145, 107174, 2021. LINK

(1) Z. Ning, L. Zhang, G. Feng, and A. Mesbah. Observation for Markov jump piecewise-affine systems with admissible region-switching paths, IEEE Transactions on Automatic Control, 66 (9), 4319-4326, 2021. LINK

2020

(14) J.A. Paulson and A. Mesbah. Data-driven scenario optimization for automated controller tuning with probabilistic performance guarantees, IEEE Control Systems Letters, 5 (4), 1477-1482, 2020. LINK

(13) D. Rodrigues, G. Makrygiorgos, and A. Mesbah. Tractable global solutions to Bayesian optimal experiment design. In Proceedings of the 59th IEEE Conference on Decision and Control, 1614-1619, 2020, Jeju Island, Republic of Korea. LINK

(12) J.A. Paulson and A. Mesbah. A Low-complexity tube controller using contractive invariant sets. In Proceedings of the 59th IEEE Conference on Decision and Control, 899-904, 2020, Jeju Island, Republic of Korea. LINK

(11) A.D. Bonzanini, J.A. Paulson, and A. Mesbah. Safe learning-based model predictive control under state- and input-dependent uncertainty using scenario trees. In Proceedings of the 59th IEEE Conference on Decision and Control, 2448-2454, 2020, Jeju Island, Republic of Korea. LINK

(10) Z. Ning, L. Zhang, A. Mesbah, and P. Colaneri. Stability analysis and stabilization of discrete-time non-homogeneous semi-Markov jump linear systems: A polytopic approach, Automatica, 120, 109080, 2020. LINK

(9) A. Mesbah, J.A. Paulson, and R.D. Braatz. An internal model control design method for failure-tolerant control with multiple objectives, Computers and Chemical Engineering, 140, 106955, 2020. LINK

(8) J.A. Paulson and A. Mesbah. Approximate closed-loop robust model predictive control with guaranteed stability and constraint satisfaction, IEEE Control Systems Letters, 4 (3), 719-724, 2020. LINK

(7) G. Makrygiorgos, G.M. Maggioni, and Ali Mesbah. Surrogate modeling for fast uncertainty quantification: Application to 2D population balance models, Computers and Chemical Engineering, 138, 106814, 2020. LINK

(6) A.D. Bonzanini and A. Mesbah. Learning-based stochastic model predictive control with state-dependent uncertainty, In Proceedings of Machine Learning Research, 120, 1-10, 2020. LINK

(5) G. Makrygiorgos, S.S. Gupta, A. Menezes, and A. Mesbah. Fast probabilistic uncertainty quantification and sensitivity analysis of a Mars life support system model. In Proceedings of the IFAC World Congress, 7358-7363, 2020, Berlin, Germany. LINK

(4) F. Petzke, A. Mesbah, and S. Streif. PoCET: a polynomial chaos expansion toolbox for Matlab. In Proceedings of the IFAC World Congress, 7346-7351, 2020, Berlin, Germany. LINK

(3) T.LM. Santos, V.M. Cunha, and A. Mesbah. Stochastic model predictive control with adaptive chance constraints based on empirical cumulative distribution. In Proceedings of the IFAC World Congress, 11408-11414, 2020, Berlin, Germany. LINK

(2) A.D. Bonzanini, J.A. Paulson, D.B. Graves, and A. Mesbah. Toward safe dose delivery in plasma medicine using projected neural network-based fast approximate NMPC. In Proceedings of the IFAC World Congress, 5353-5359, 2020, Berlin, Germany. LINK

(1) J. O’Leary, K. Sawlani, and A. Mesbah. Deep learning for classification of the chemical composition of particle defects on semiconductor wafers, IEEE Transactions on Semiconductor Manufacturing, 33 (1), 72-85, 2020. LINK

2019

(14) J.A. Paulson, T.L.M. Santos, and A. Mesbah. Mixed stochastic-deterministic tube MPC for offset-free tracking in the presence of plant-model mismatch, Journal of Process Control , 83, 102-120, 2019. LINK

(13) J.A. Paulson, M. Martin-Casas, and A. Mesbah. Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions, PLOS Computational Biology, 15, e1007308, 2019. LINK

(12) M. Witman, D. Gidon, D.B. Graves, B. Smit, and A. Mesbah. Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet, Plasma Sources Science and Technology, 28, 095019, 2019. LINK

(11) D. Gidon, D.B. Graves, and A. Mesbah. Predictive control of 2D spatial thermal dose delivery in atmospheric pressure plasma jets, Plasma Sources Science and Technology, 28, 085001, 2019. LINK

(10) J.A. Paulson, T.A.N. Heirung, and A. Mesbah. Fault-tolerant tube-based robust nonlinear model predictive control. In Proceedings of the American Control Conference, 1648-1654, 2019, Philadelphia. LINK

(9) T.L.M. Santos, A.D. Bonzanini, T.A.N. Heirung, and A. Mesbah. A constraint-tightening approach to nonlinear model predictive control with chance constraints for stochastic systems. In Proceedings of the American Control Conference, 1641-1647, 2019, Philadelphia. LINK

(8) A.D. Bonzanini, T.L.M. Santos and A. Mesbah. Tube-based stochastic nonlinear model predictive control: A comparative study on constraint tightening. In Proceedings of the 12th IFAC Symposium on Dynamics and Control of Process Systems (DYCOPS), 598-603, 2019, Florianópolis, Brazil. LINK

(7) T.A.N. Heirung, T.LM. Santos, and A. Mesbah. Model predictive control with active learning for stochastic systems with structural model uncertainty: Online Model Discrimination, Computers and Chemical Engineering, 128, 128-140, 2019. LINK

(6) A. Mesbah and D.B. Graves. Machine learning for modeling, diagnostics, and control of non-equilibrium plasmas, Journal of Physics D, 52, 30LT02, 2019. LINK

(5) D. Gidon, X. Pei, A.D. Bonzanini, D.B. Graves, and A. Mesbah, Machine learning for real-time diagnostics of cold atmospheric plasma sources, IEEE Transactions on Radiation and Plasma Medical Sciences, 3, 597-605, 2019. LINK

(4) T.A.N. Heirung and A. Mesbah. Input design for active fault diagnosis, Annual Reviews in Control, 47, 35-50, 2019. LINK

(3) D. Gidon, D.B. Graves, and A. Mesbah. Spatial thermal dose delivery in atmospheric pressure plasma jets, Plasma Sources Science and Technology, 28, 025006, 2019. LINK

(2) J.A. Paulson, M. Martin-Casas, and A. Mesbah. Optimal Bayesian experiment design for nonlinear dynamic systems with chance constraints, Journal of Process Control, 77, 155-171, 2019. LINK

(1) J.A. Paulson and A. Mesbah. An efficient method for stochastic optimal control with joint chance constraints for nonlinear systems. International Journal of Robust and Nonlinear Control, 29, 5017-5037, 2019. LINK

2018

(10) J.A. Paulson and A. Mesbah. Shaping the closed-loop behavior of nonlinear systems under probabilistic uncertainty using arbitrary polynomial chaos. In Proceedings of the 57th IEEE Conference on Decision and Control, 6307-6713, 2018, Miami. LINK

(9) J.A. Paulson and A. Mesbah. Nonlinear Model Predictive Control with Explicit Backoffs for Stochastic Systems under Arbitrary Uncertainty. In Proceedings of the 6th IFAC Conference on Nonlinear Model Predictive Control, 622-633, 2018, Madison. LINK

(8) T.A.N. Heirung, J.A. Paulson, S. Lee, and A. Mesbah. Model predictive control with active learning under model uncertainty: Why, when and how, AIChE Journal, 64 (8), 3071-3081, 2018. LINK

(7) T.L.M. Santos, J.A. Paulson, and A. Mesbah. Stochastic model predictive control with enlarged domain of attraction for offset-free tracking. In Proceedings of the American Control Conference, 742-748, 2018, Milwaukee. LINK

(6) J.A. Paulson, T.A.N. Heirung, R.D. Braatz, and A. Mesbah. Closed-loop active fault diagnosis for stochastic linear systems. In Proceedings of the American Control Conference, 735-741, 2018, Milwaukee. LINK

(5) M. Martin-Casas and A. Mesbah. Active fault diagnosis for stochastic nonlinear systems: Online probabilistic model discrimination. In Proceedings of the IFAC International Symposium on Advanced Control of Chemical Processes, 696-701, 2018, Shenyang. LINK

(4) A. Mesbah. Stochastic model predictive control with active uncertainty learning: A survey on dual control, Annual Reviews in Control, 45, 107-117, 2018. LINK

(3) T.A.N. Heirung, J.A. Paulson, J. O'Leary, and A. Mesbah. Stochastic model predictive control — how does it work?, Computers and Chemical Engineering, 114, 158-170, 2018. LINK

(2) D. Gidon, B. Curtis, J.A. Paulson, D.B. Graves, and A. Mesbah. Model-based feedback control of a kHz-excited atmospheric pressure plasma jet. IEEE Transactions on Radiation and Plasma Medical Sciences, 2, 129-137, 2018. LINK

(1) K.Georgiou, J. Harte, A. Mesbah, and W.J. Riley. A method of alternating characteristics with application to advection-dominated environmental systems, Computational Geosciences, 1-15, 2018. LINK

2017

(9) G.R. Marseglia D.M. Raimundo, L. Magni and A. Mesbah. A probabilistic framework for reference design for guaranteed fault diagnosis under closed-loop control. In Proceedings of the 56th IEEE Conference on Decision and Control, 5739-5744, 2017, Melbourne. LINK

(8) J.A. Paulson, M. Martin-Casas and A. Mesbah. Input design for online fault diagnosis of nonlinear systems with stochastic uncertainty, Industrial and Engineering Chemistry Research, 56, 9593-9605, 2017. LINK

(7) D. Gidon, D. B. Graves, and A. Mesbah. Effective dose delivery in atmospheric pressure plasma jets for plasma medicine: a model predictive control approach, Plasma Sources Science and Technology, 26, 085005, 2017. LINK

(6) J.A. Paulson, E.A. Buehler, and A. Mesbah. Arbitrary polynomial chaos for uncertainty propagation of correlated random variables in dynamic systems. In Proceedings of the IFAC World Congress, 3607-3612, 2017, Toulouse. LINK

(5) J.A. Paulson, L. Xie, and A. Mesbah. Offset-free robust MPC of systems with mixed stochastic and deterministic uncertainty. In Proceedings of the IFAC World Congress, 3589-3594, 2017, Toulouse. LINK

(4) T.A.N. Heirung and A. Mesbah. Stochastic nonlinear model predictive control with active model discrimination: a closed-loop fault diagnosis application. In Proceedings of the IFAC World Congress, 16504-16509, 2017, Toulouse. LINK

(3) A. Mesbah, J.A. Paulson, R. Lakerveld, and R.D. Braatz. Model predictive control of an integrated continuous pharmaceutical manufacturing pilot plant, Organic Process Research & Development, 21, 844-854, 2017. LINK

(2) J.A. Paulson, E.A. Buehler, R.D. Braatz, and A. Mesbah. Stochastic model predictive control with joint chance constraints, International Journal of Control, 1-14, 2017. LINK

(1) T.A.N. Heirung and A. Mesbah. Perspectives on Stochastic predictive control with autonomous model adaptation for model structure uncertainty. In Proceedings of the Chemical Process Control Conference, 1-6, 2017, Tucson. LINK

2016

(9) M. Martin-Casas and A. Mesbah. Discrimination between competing model structures of biological systems in the presence of population heterogeneity, IEEE Life Science Letters, 2 (3), 23-26, 2016. LINK

(8) A. Mesbah. Stochastic model predictive control: An overview and perspectives for future research, IEEE Control Systems, 36, 30-44, 2016. LINK

(7) V. A. Bavdekar, V. Ehlinger, D. Gidon, and A. Mesbah. Stochastic predictive control with adaptive model maintenance. In Proceedings of the 55th IEEE Conference on Decision and Control, 2745-2750, 2016, Las Vegas. LINK

(6) V. Bavdekar and A. Mesbah. Stochastic nonlinear model predictive control with joint chance constraints. In Proceedings of the 10th IFAC Symposium on Nonlinear Control Systems, 276-281, 2016, Monterey. LINK

(5) E. A. Buehler and A. Mesbah. Kinetic study of acetone-butanol-ethanol fermentation in continuous culture, PLOS ONE, 1-21, 2016. LINK

(4) E. A. Buehler, J. A. Paulson, and A. Mesbah. Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions. In Proceedings of the American Control Conference, 5389-5394, 2016, Boston. LINK

(3) V. Bavdekar and A. Mesbah. A polynomial chaos-based nonlinear Bayesian approach for estimating state and parameter probability distribution functions. In Proceedings of the American Control Conference, 2047-2052, 2016, Boston. LINK

(2) D. Gidon, D. B. Graves, and A. Mesbah. Model predictive control of thermal effects of an atmospheric pressure plasma jet for biomedical applications. In Proceedings of the American Control Conference, 4889-4894, 2016, Boston. LINK

(1) V. Bavdekar and A. Mesbah. Stochastic model predictive control with integrated experiment design for nonlinear systems. In Proceedings of the 11th IFAC Symposium on Dynamics and Control of Process Systems (DYCOPS), 49-54, 2016, Trondheim. LINK

2015

(6) J.A. Paulson, S. Streif, and A. Mesbah. Stability for receding-horizon stochastic model predictive control. In Proceedings of the American Control Conference, 937-943, 2015, Chicago. LINK

(5) A. Mesbah, J. A. Paulson, R. Lakerveld, and R. D. Braatz. Plant-wide model predictive control for a continuous pharmaceutical process. In Proceedings of the American Control Conference, 4301-4307, 2015, Chicago. LINK

(4) A. Mesbah and S. Streif. A probabilistic approach to robust optimal experiment design with chance constraints. In Proceedings of the 9th International Symposium on Advanced Control of Chemical Processes , 100-105, 2015, Whistler. LINK

(3) A. Mesbah, X. Bombois, M. Forgione, H. Hjalmarsson, and P.M.J. Van den Hof. Least costly closed-loop performance diagnosis and plant re-identification, International Journal of Control, 1-13, 2015. LINK

(2) M. Forgione, G. Birpoutsoukis, X. Bombois, A. Mesbah, P.J. Daudey , P.M.J. Van den Hof. Batch-to-batch model improvement for cooling crystallization, Control Engineering Practice, 41, 72-82, 2015. LINK

(1) J. A. Paulson, A. Mesbah, X. Zhu, M.C. Molaro, and R. D. Braatz. Control of self-assembly in micro- and nano-scale systems, Journal of Process Control, 27, 38-49, 2015. LINK

2014

(7) J. A. Paulson, A. Mesbah, S. Streif, R. Findeisen, and R. D. Braatz. Fast stochastic model predictive control of high-dimensional systems. In Proceedings of the 53rd IEEE Conference on Decision and Control, 2802-2809, 2014, Los Angeles. LINK

(6) A. Mesbah, S. Streif, R. Findeisen, and R. D. Braatz. Active fault diagnosis for nonlinear systems with probabilistic uncertainties. In Proceedings of the IFAC World Congress, 7079-7084, 2014, Cape Town. LINK

(5) S. Streif, F. Petzke, A. Mesbah, R. Findeisen, and R. D. Braatz. Optimal experimental design for probabilistic model discrimination using polynomial chaos. In Proceedings of the IFAC World Congress, 4103-4109, 2014, Cape Town. LINK

(4) A. A. Bachnas, R. Toth, J. H. A. Ludlage, and A. Mesbah. A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study, Journal of Process Control, 24, 272-285, 2014. LINK

(3) X. Bombois, M. Potters, and A. Mesbah. Closed-loop performance diagnosis of model predictive control systems. In Proceedings of the European Control Conference, 264-269, 2014, Strasbourg. LINK

(2) A. Mesbah, S. Streif, R. Findeisen, and R. D. Braatz. Stochastic nonlinear model predictive control with probabilistic constraints. In Proceedings of the American Control Conference, 2413-2419, 2014, Portland. LINK

(1) A. Mesbah, A. N. Ford Versypt, X. Zhu, and R. D. Braatz. Nonlinear model-based control of thin-film drying for continuous pharmaceutical manufacturing, Industrial & Engineering Chemistry Research, 7447-7460, 2014. LINK

2013 and Earlier

(25) A. Mesbah, M. Kishida, and R. D. Braatz. Design of multi-objective failure-tolerant control systems for infinite-dimensional systems. In Proceedings of the 52nd IEEE Conference on Decision and Control, 3006-3013, 2013, Florence. LINK

(24) A. Mesbah and R. D. Braatz. Design of multi-objective control systems with optimal failure tolerance. In Proceedings of the European Control Conference, 2963-2968, 2013, Zurich. LINK

(23) A. A. Bachnas, R. Toth, A. Mesbah, and J. Ludlage. Perspectives of data-driven LPV modeling of high-purity distillation columns. In Proceedings of the European Control Conference, 3776-3783, 2013, Zurich. LINK

(22) C. A. Larsson, M. Annergren, H. Hjalmarsson, C. R. Rojas, X. Bombois, A. Mesbah, and P. E. Moden. Model predictive control with integrated experiment design for output error systems. In Proceedings of the European Control Conference, 3790-3795, 2013, Zurich. LINK

(21) A. Mesbah, Z. K. Nagy, A. E. M. Huesman, H. J. M. Kramer, and P. M. J. Van den Hof. Nonlinear model-based control of a semi-industrial batch crystallizer using a population balance modeling framework. IEEE Transactions on Control Systems Technology, 20, 1188-1201, 2012. LINK

(20) A. Mesbah, X. Bombois, M. Forgione, J. H. A. Ludlage, P. E. Moden, H. Hjalmarsson, and P. M. J. Van den Hof. A unified experiment design framework for detection and identification in closed-loop performance diagnosis. In Proceedings of the 51st IEEE Conference on Decision and Control, 2152-2157, 2012, Maui. LINK

(19) M. Forgione, A. Mesbah, X. Bombois, and P. M. J. Van den Hof. Batch-to-batch strategies for cooling crystallization. In Proceedings of the 51st IEEE Conference on Decision and Control, 6364-6369, 2012, Maui. LINK

(18) A. Mesbah, X. Bombois, J. H. A. Ludlage, and P. M. J. Van den Hof. Experiment design for closed-loop performance diagnosis. In Proceedings of the 16th IFAC Symposium on System Identification, 1341-1346, 2012, Brussels. LINK

(17) M. Forgione, A. Mesbah, X. Bombois, and P. M. J. Van den Hof. Iterative learning control of supersaturation in batch cooling crystallization. In Proceedings of the American Control Conference, 6455-6460, 2012, Montreal. LINK

(16) A. Mesbah, X. Bombois, J. H. A. Ludlage, and P. M. J. Van den Hof. Closed-loop performance diagnosis using prediction error identification. In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2969-2974, 2011, Orlando. LINK

(15) A. Mesbah, A. E. M. Huesman, H. J. M. Kramer, and P. M. J. Van den Hof. A comparison of nonlinear observers for output feedback model-based control of seeded batch crystallization processes. Journal of Process Control, 21, 652-666, 2011. LINK

(14) A. Mesbah, A. E. M. Huesman, H. J. M. Kramer, Z. K. Nagy, and P. M. J. Van den Hof. Real-time control of a semi-industrial fed-batch evaporative crystallizer using different direct optimization strategies. AIChE Journal, 57, 1557-1569, 2011. LINK

(13) S. Kadam, A. Mesbah, E. van der Windt, and H. J. M. Kramer. Rapid online calibration for ATR-FTIR spectroscopy during Batch crystallization of ammonium sulphate in a semi-industrial scale crystallizer. Chemical Engineering Research and Design, 89, 995-1005, 2011. LINK

(12 A. Mesbah, A. E. M. Huesman, H. J. M. Kramer, and P. M. J. Van den Hof. Nonlinear state estimation for closed-loop control of batch crystallization processes. In Proceedings of the 9th International Symposium on Dynamics and Control of Process Systems, 371-376, 2010, Leuven. LINK

(11) A. Mesbah, J. Landlust, C. Versteeg, A. E. M. Huesman, H. J. M. Kramer, J. H. A. Ludlage, and P. M. J. Van den Hof. Model-based Optimal Control of Industrial batch crystallizers. In Proceedings of the 20th European Symposium on Computer Aided Process Engineering, 1563-1568, 2010. LINK

(10) A. Mesbah, J. Landlust, A. E. M. Huesman, H. J. M. Kramer, P. J. Jansens, and P. M. J. Van den Hof. A model-based control framework for industrial batch crystallization processes. Chemical Engineering Research and Design, 88, 1223-1233, 2010. LINK

(9) A. Mesbah, H. J. M. Kramer, A. E. M. Huesman, and P. M. J. Van den Hof. A control oriented study on the numerical solution of the population balance equation for crystallization processes. Chemical Engineering Science, 64, 4262-4277, 2009. LINK

(8) A. N. Ajah, A. Mesbah, J. Grievink, P. M. Herder, P. W. Falcao, and S. Wennekes. On the robustness, effectiveness and reliability of chemical and mechanical heat pumps for low-temperature heat source district heating: A comparative simulation-based analysis and evaluation. Journal of Energy, 33, 908-929, 2008. LINK

(7) A. Mesbah, A. N. Kalbasenka, A. E. M. Huesman, H. J. M. Kramer, and P. M. J. Van den Hof. Real-time dynamic optimization of batch crystallization processes. In Proceedings of the IFAC World Congress, 3246-3251, 2008, Seoul, Republic of Korea. LINK

(6) J. Landlust, A. Mesbah, J. Wildenberg, A. N. Kalbasenka, H. J. M. Kramer, and J. H. A. Ludlage. An industrial model predictive control architecture for batch crystallization. In Proceedings of the 17th International Symposium on Industrial Crystallization, 35-42, 2008, Maastricht. LINK

(5) A. Mesbah, J. Landlust, A.E.M. Huesman, H.J.M. Kramer, P.M.J. Van den Hof, and P.J. Jansens. Model-based optimal operation of seeded batch crystallization processes. In Proceedings of the 17th International Symposium on Industrial Crystallization, 721-728, 2008, Maastricht. LINK

(4) F. Abdolahi Demneh, A. Mesbah, and A. Jaberi. Comparative evaluation of natural gas pipeline simulators. PetroMin, September Issue, 6-8, 2008. LINK

(3) F. A. Demneh and A. Mesbah. The effect of kinetic energy change on flow in gas pipelines. Journal of Hydrocarbon Processing, May Issue, 1-4, 2008. LINK

(2) F. Abdolahi, A. Mesbah, R. B. Boozarjomehry, and W. Y. Svrcek. The effect of major parameters on simulation results of gas pipelines. International Journal of Mechanical Sciences, 49, 989-1000, 2007. LINK

(1) A. Mesbah and F. Abdolahi. Natural gas supply cut-off in gas distribution networks. PetroMin, September Issue, 34-36, 2006. LINK