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Ali Mesbah 0002
Person information
- affiliation: University of California, Berkeley, Department of Chemical and Biomolecular Engineering, CA, USA
- affiliation (2012 - 2014): Massachusetts Institute of Technology, Cambridge, MA, USA
- affiliation (PhD 2010): Delft University of Technology, The Netherlands
Other persons with the same name
- Ali Mesbah 0001
— University of British Columbia, Department of Electrical and Computer Engineering, BC, Canada
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2020 – today
- 2024
- [j27]Angelo D. Bonzanini, Ali Mesbah, Stefano Di Cairano:
Perception-aware model predictive control for constrained control in unknown environments. Autom. 160: 111418 (2024) - [j26]Prodromos Daoutidis
Machine learning in process systems engineering: Challenges and opportunities. Comput. Chem. Eng. 181: 108523 (2024) - [j25]Kwanghyun Cho, Ketong Shao
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. Comput. Chem. Eng. 185: 108653 (2024) - [j24]Iman Nodozi
Neural Schrödinger Bridge With Sinkhorn Losses: Application to Data-Driven Minimum Effort Control of Colloidal Self-Assembly. IEEE Trans. Control. Syst. Technol. 32(3): 960-973 (2024) - [j23]Kimberly J. Chan, Joel A. Paulson, Ali Mesbah:
A Practical Multiobjective Learning Framework for Optimal Hardware-Software Co-Design of Control-on-a-Chip Systems. IEEE Trans. Control. Syst. Technol. 32(6): 2178-2193 (2024) - [i14]Sebastian Hirt, Maik Pfefferkorn, Ali Mesbah, Rolf Findeisen:
Stability-informed Bayesian Optimization for MPC Cost Function Learning. CoRR abs/2404.12187 (2024) - [i13]Alessandro N. Vargas, Victor Miller, Ali Mesbah, Gabriele Neretti:
Low-cost sensors and circuits for plasma education: characterizing power and illuminance. CoRR abs/2405.02510 (2024) - [i12]Joshua Hang Sai Ip, Georgios Makrygiorgos, Ali Mesbah:
Lyapunov Neural ODE Feedback Control Policies. CoRR abs/2409.00393 (2024) - 2023
- [j22]Pouria Karimi Shahri
Traffic Congestion Control Using Distributed Extremum Seeking and Filtered Feedback Linearization Control Approaches. IEEE Control. Syst. Lett. 7: 1003-1008 (2023) - [j21]Raffaele Soloperto
Safe Exploration and Escape Local Minima With Model Predictive Control Under Partially Unknown Constraints. IEEE Trans. Autom. Control. 68(12): 7530-7545 (2023) - [j20]Diogo Rodrigues
Data-Driven Adaptive Optimal Control Under Model Uncertainty: An Application to Cold Atmospheric Plasmas. IEEE Trans. Control. Syst. Technol. 31(1): 55-69 (2023) - [c41]Iman Nodozi, Jared O'Leary
A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly. ACC 2023: 609-615 - [c40]Joel A. Paulson, Farshud Sorourifar, Ali Mesbah:
A Tutorial on Derivative-Free Policy Learning Methods for Interpretable Controller Representations. ACC 2023: 1295-1306 - [c39]Kimberly J. Chan, Georgios Makrygiorgos, Ali Mesbah:
Towards Personalized Plasma Medicine via Data-Efficient Adaptation of Fast Deep Learning-based MPC Policies. ACC 2023: 2769-2775 - [c38]Jared O'Leary
Novelty Search for Neuroevolutionary Reinforcement Learning of Deceptive Systems: An Application to Control of Colloidal Self-assembly. ACC 2023: 2776-2781 - [c37]Georgios Makrygiorgos, Joel A. Paulson, Ali Mesbah:
No-Regret Bayesian Optimization with Gradients Using Local Optimality-Based Constraints: Application to Closed-Loop Policy Search. CDC 2023: 20-25 - [c36]Kimberly J. Chan, Joel A. Paulson, Ali Mesbah:
Safe Explorative Bayesian Optimization - Towards Personalized Treatments in Plasma Medicine. CDC 2023: 4106-4111 - [i11]Iman Nodozi, Charlie Yan, Mira M. Khare, Abhishek Halder, Ali Mesbah:
Neural Schrödinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly. CoRR abs/2307.14442 (2023) - 2022
- [j19]Georgios Makrygiorgos, Angelo D. Bonzanini
Performance-oriented model learning for control via multi-objective Bayesian optimization. Comput. Chem. Eng. 162: 107770 (2022) - [j18]Jared O'Leary
Stochastic physics-informed neural ordinary differential equations. J. Comput. Phys. 468: 111466 (2022) - [j17]Diogo Rodrigues
Efficient Global Solutions to Single-Input Optimal Control Problems via Approximation by Sum-of-Squares Polynomials. IEEE Trans. Autom. Control. 67(9): 4674-4686 (2022) - [j16]Angelo D. Bonzanini
Learning-Based SMPC for Reference Tracking Under State-Dependent Uncertainty: An Application to Atmospheric Pressure Plasma Jets for Plasma Medicine. IEEE Trans. Control. Syst. Technol. 30(2): 611-624 (2022) - [c35]Ali Mesbah, Kim Peter Wabersich, Angela P. Schoellig, Melanie N. Zeilinger, Sergio Lucia, Thomas A. Badgwell, Joel A. Paulson:
Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon? ACC 2022: 342-357 - [c34]Angelo D. Bonzanini, Ali Mesbah, Stefano Di Cairano:
Multi-stage Perception-aware Chance-constrained MPC with Applications to Automated Driving. ACC 2022: 1697-1702 - [c33]Yajie Bao
Learning-based Adaptive-Scenario-Tree Model Predictive Control with Probabilistic Safety Guarantees Using Bayesian Neural Networks. ACC 2022: 3260-3265 - [c32]Angelo D. Bonzanini, Joel A. Paulson, Georgios Makrygiorgos, Ali Mesbah:
Scalable Estimation of Invariant Sets for Mixed-Integer Nonlinear Systems using Active Deep Learning. CDC 2022: 3431-3437 - [i10]Iman Nodozi, Jared O'Leary, Ali Mesbah, Abhishek Halder:
A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly. CoRR abs/2208.09182 (2022) - 2021
- [j15]Angelo D. Bonzanini, Joel A. Paulson, Georgios Makrygiorgos, Ali Mesbah:
Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks. Comput. Chem. Eng. 145: 107174 (2021) - [j14]Joel A. Paulson
Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees. IEEE Control. Syst. Lett. 5(4): 1477-1482 (2021) - [j13]Zepeng Ning
Observation for Markov Jump Piecewise-Affine Systems With Admissible Region-Switching Paths. IEEE Trans. Autom. Control. 66(9): 4319-4326 (2021) - [c31]Angelo D. Bonzanini, Ali Mesbah, Stefano Di Cairano:
Perception-Aware Chance-Constrained Model Predictive Control for Uncertain Environments. ACC 2021: 2082-2087 - [c30]Joel A. Paulson, Ali Mesbah:
Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees. ACC 2021: 2102-2107 - [c29]Kimberly J. Chan
Deep Learning-based Approximate Nonlinear Model Predictive Control with Offset-free Tracking for Embedded Applications. ACC 2021: 3475-3481 - [c28]Angelo D. Bonzanini, Ali Mesbah, Stefano Di Cairano:
On the Stability Properties of Perception-aware Chance-constrained MPC in Uncertain Environments. CDC 2021: 2177-2182 - [c27]Joel A. Paulson, Ketong Shao, Ali Mesbah:
Probabilistically Robust Bayesian Optimization for Data-Driven Design of Arbitrary Controllers with Gaussian Process Emulators. CDC 2021: 3633-3639 - [i9]Jared O'Leary, Joel A. Paulson, Ali Mesbah:
Stochastic Physics-Informed Neural Networks (SPINN): A Moment-Matching Framework for Learning Hidden Physics within Stochastic Differential Equations. CoRR abs/2109.01621 (2021) - 2020
- [j12]Zepeng Ning, Lixian Zhang, Ali Mesbah, Patrizio Colaneri
Stability analysis and stabilization of discrete-time non-homogeneous semi-Markov jump linear systems: A polytopic approach. Autom. 120: 109080 (2020) - [j11]Georgios Makrygiorgos, Giovanni Maria Maggioni, Ali Mesbah:
Surrogate modeling for fast uncertainty quantification: Application to 2D population balance models. Comput. Chem. Eng. 138: 106814 (2020) - [j10]Ali Mesbah, Joel A. Paulson, Richard D. Braatz
An internal model control design method for failure-tolerant control with multiple objectives. Comput. Chem. Eng. 140: 106955 (2020) - [j9]Joel A. Paulson
Approximate Closed-Loop Robust Model Predictive Control With Guaranteed Stability and Constraint Satisfaction. IEEE Control. Syst. Lett. 4(3): 719-724 (2020) - [j8]Joel A. Paulson
Stochastic model predictive control with joint chance constraints. Int. J. Control 93(1): 126-139 (2020) - [c26]Joel A. Paulson, Ali Mesbah:
A Low-complexity Tube Controller using Contractive Invariant Sets. CDC 2020: 899-904 - [c25]Diogo Rodrigues
Tractable Global Solutions to Bayesian Optimal Experiment Design. CDC 2020: 1614-1619 - [c24]Angelo D. Bonzanini, Joel A. Paulson, Ali Mesbah:
Safe Learning-based Model Predictive Control under State- and Input-dependent Uncertainty using Scenario Trees. CDC 2020: 2448-2454 - [c23]Angelo Domenico Bonzanini, Ali Mesbah:
Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty. L4DC 2020: 571-580 - [i8]Felix Petzke, Ali Mesbah, Stefan Streif:
PoCET: a Polynomial Chaos Expansion Toolbox for Matlab. CoRR abs/2007.05245 (2020) - [i7]Joel A. Paulson, Ali Mesbah:
Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees. CoRR abs/2011.07445 (2020) - [i6]Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah, Joel A. Paulson:
A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization. CoRR abs/2011.11841 (2020)
2010 – 2019
- 2019
- [j7]Tor Aksel N. Heirung, Ali Mesbah:
Input design for active fault diagnosis. Annu. Rev. Control. 47: 35-50 (2019) - [j6]Tor Aksel N. Heirung, Tito L. M. Santos
Model predictive control with active learning for stochastic systems with structural model uncertainty: Online model discrimination. Comput. Chem. Eng. 128: 128-140 (2019) - [j5]Joel A. Paulson
Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions. PLoS Comput. Biol. 15(8) (2019) - [c22]Tito L. M. Santos
A Constraint-Tightening Approach to Nonlinear Model Predictive Control with Chance Constraints for Stochastic Systems. ACC 2019: 1641-1647 - [c21]Joel A. Paulson, Tor Aksel N. Heirung, Ali Mesbah:
Fault-Tolerant Tube-Based Robust Nonlinear Model Predictive Control. ACC 2019: 1648-1654 - 2018
- [j4]Ali Mesbah:
Stochastic model predictive control with active uncertainty learning: A Survey on dual control. Annu. Rev. Control. 45: 107-117 (2018) - [j3]Tor Aksel N. Heirung
Stochastic model predictive control - how does it work? Comput. Chem. Eng. 114: 158-170 (2018) - [c20]Joel A. Paulson
Closed-Loop Active Fault Diagnosis for Stochastic Linear Systems. ACC 2018: 735-741 - [c19]Tito L. M. Santos
Stochastic Model Predictive Control with Enlarged Domain of Attraction for Offset-Free Tracking. ACC 2018: 742-748 - [c18]Joel A. Paulson
Shaping the Closed-Loop Behavior of Nonlinear Systems Under Probabilistic Uncertainty Using Arbitrary Polynomial Chaos. CDC 2018: 6307-6313 - 2017
- [c17]Giuseppe Roberto Marseglia, Davide Martino Raimondo, Lalo Magni, Ali Mesbah:
A probabilistic framework for reference design for guaranteed fault diagnosis under closed-loop control. CDC 2017: 5739-5744 - 2016
- [c16]Vinay A. Bavdekar, Ali Mesbah:
A polynomial chaos-based nonlinear Bayesian approach for estimating state and parameter probability distribution functions. ACC 2016: 2047-2052 - [c15]Dogan Gidon
Model predictive control of thermal effects of an atmospheric pressure plasma jet for biomedical applications. ACC 2016: 4889-4894 - [c14]Edward A. Buehler, Joel A. Paulson
Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions. ACC 2016: 5389-5394 - [c13]Vinay A. Bavdekar, Victoria Ehlinger
Stochastic predictive control with adaptive model maintenance. CDC 2016: 2745-2750 - 2015
- [j2]Ali Mesbah, Xavier Bombois, Marco Forgione
Least costly closed-loop performance diagnosis and plant re-identification. Int. J. Control 88(11): 2264-2276 (2015) - [c12]Joel A. Paulson
Stability for receding-horizon stochastic model predictive control. ACC 2015: 937-943 - [c11]Ali Mesbah, Joel A. Paulson
Plant-wide model predictive control for a continuous pharmaceutical process. ACC 2015: 4301-4307 - [i5]Edward A. Buehler, Joel A. Paulson, Ali Akhavan, Ali Mesbah:
Lyapunov-based Stochastic Nonlinear Model Predictive Control: Shaping the State Probability Density Functions. CoRR abs/1505.02871 (2015) - [i4]Joel A. Paulson, Edward A. Buehler, Richard D. Braatz, Ali Mesbah:
Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints. CoRR abs/1506.08471 (2015) - 2014
- [c10]Ali Mesbah, Stefan Streif
Stochastic nonlinear model predictive control with probabilistic constraints. ACC 2014: 2413-2419 - [c9]Joel A. Paulson
Fast stochastic model predictive control of high-dimensional systems. CDC 2014: 2802-2809 - [i3]Stefan Streif, Matthias Karl, Ali Mesbah:
Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints. CoRR abs/1410.4535 (2014) - [i2]Ali Mesbah, Stefan Streif:
Stability for Receding-horizon Stochastic Model Predictive Control with Chance Constraints. CoRR abs/1410.5083 (2014) - [i1]Ali Mesbah, Stefan Streif:
A Probabilistic Approach to Robust Optimal Experiment Design with Chance Constraints. CoRR abs/1411.2683 (2014) - 2013
- [c8]Ali Mesbah, Masako Kishida
Design of multi-objective failure-tolerant control systems for infinite-dimensional systems. CDC 2013: 3006-3013 - [c7]Ali Mesbah, Richard D. Braatz:
Design of multi-objective control systems with optimal failure tolerance. ECC 2013: 2963-2968 - [c6]A. A. Bachnas, Roland Tóth, Ali Mesbah, Jobert H. A. Ludlage:
Perspectives of data-driven LPV modeling of high-purity distillation columns. ECC 2013: 3776-3783 - [c5]Christian A. Larsson, Mariette Annergren, Håkan Hjalmarsson
Model predictive control with integrated experiment design for output error systems. ECC 2013: 3790-3795 - 2012
- [j1]Ali Mesbah, Zoltán K. Nagy, Adrie E. M. Huesman, Herman J. M. Kramer, Paul M. J. Van den Hof
Nonlinear Model-Based Control of a Semi-Industrial Batch Crystallizer Using a Population Balance Modeling Framework. IEEE Trans. Control. Syst. Technol. 20(5): 1188-1201 (2012) - [c4]Marco Forgione
Iterative Learning Control of supersaturation in batch cooling crystallization. ACC 2012: 6455-6460 - [c3]Ali Mesbah, Xavier Bombois, Marco Forgione
A unified experiment design framework for detection and identification in closed-loop performance diagnosis. CDC 2012: 2152-2157 - [c2]Marco Forgione
Batch-to-batch strategies for cooling crystallization. CDC 2012: 6364-6369 - 2011
- [c1]Ali Mesbah, Xavier Bombois, Jobert H. A. Ludlage, Paul M. J. Van den Hof
Closed-loop performance diagnosis using prediction error identification. CDC/ECC 2011: 2969-2974
2000 – 2009
- 2009
- [b1]Ali Mesbah:
Analysis and Testing of Ajax-based Single-page Web Applications. Delft University of Technology, Netherlands, 2009
Coauthor Index
Angelo D. Bonzanini
aka: Angelo Domenico Bonzanini
aka: Angelo Domenico Bonzanini
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