Ali Mesbah
Orcid: 0000-0002-1700-0600Affiliations:
- University of California, Berkeley, Department of Chemical and Biomolecular Engineering, CA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA (2012 - 2014)
- Delft University of Technology, The Netherlands (PhD 2010)
According to our database1,
Ali Mesbah
authored at least 81 papers
between 2009 and 2025.
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Bibliography
2025
CoRR, January, 2025
2024
A Practical Multiobjective Learning Framework for Optimal Hardware-Software Co-Design of Control-on-a-Chip Systems.
IEEE Trans. Control. Syst. Technol., November, 2024
Neural Schrödinger Bridge With Sinkhorn Losses: Application to Data-Driven Minimum Effort Control of Colloidal Self-Assembly.
IEEE Trans. Control. Syst. Technol., May, 2024
Comput. Chem. Eng., February, 2024
Perception-aware model predictive control for constrained control in unknown environments.
Autom., February, 2024
Coactive Preference-Guided Multi-Objective Bayesian Optimization: An Application to Policy Learning in Personalized Plasma Medicine.
IEEE Control. Syst. Lett., 2024
Low-cost sensors and circuits for plasma education: characterizing power and illuminance.
CoRR, 2024
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., 2024
2023
Safe Exploration and Escape Local Minima With Model Predictive Control Under Partially Unknown Constraints.
IEEE Trans. Autom. Control., December, 2023
Data-Driven Adaptive Optimal Control Under Model Uncertainty: An Application to Cold Atmospheric Plasmas.
IEEE Trans. Control. Syst. Technol., 2023
Traffic Congestion Control Using Distributed Extremum Seeking and Filtered Feedback Linearization Control Approaches.
IEEE Control. Syst. Lett., 2023
No-Regret Bayesian Optimization with Gradients Using Local Optimality-Based Constraints: Application to Closed-Loop Policy Search.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023
Safe Explorative Bayesian Optimization - Towards Personalized Treatments in Plasma Medicine.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023
A Tutorial on Derivative-Free Policy Learning Methods for Interpretable Controller Representations.
Proceedings of the American Control Conference, 2023
Novelty Search for Neuroevolutionary Reinforcement Learning of Deceptive Systems: An Application to Control of Colloidal Self-assembly.
Proceedings of the American Control Conference, 2023
A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly.
Proceedings of the American Control Conference, 2023
Towards Personalized Plasma Medicine via Data-Efficient Adaptation of Fast Deep Learning-based MPC Policies.
Proceedings of the American Control Conference, 2023
2022
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., 2022
Efficient Global Solutions to Single-Input Optimal Control Problems via Approximation by Sum-of-Squares Polynomials.
IEEE Trans. Autom. Control., 2022
J. Comput. Phys., 2022
Performance-oriented model learning for control via multi-objective Bayesian optimization.
Comput. Chem. Eng., 2022
Scalable Estimation of Invariant Sets for Mixed-Integer Nonlinear Systems using Active Deep Learning.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022
Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon?
Proceedings of the American Control Conference, 2022
Multi-stage Perception-aware Chance-constrained MPC with Applications to Automated Driving.
Proceedings of the American Control Conference, 2022
Learning-based Adaptive-Scenario-Tree Model Predictive Control with Probabilistic Safety Guarantees Using Bayesian Neural Networks.
Proceedings of the American Control Conference, 2022
2021
Observation for Markov Jump Piecewise-Affine Systems With Admissible Region-Switching Paths.
IEEE Trans. Autom. Control., 2021
Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees.
IEEE Control. Syst. Lett., 2021
Stochastic Physics-Informed Neural Networks (SPINN): A Moment-Matching Framework for Learning Hidden Physics within Stochastic Differential Equations.
CoRR, 2021
Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks.
Comput. Chem. Eng., 2021
Probabilistically Robust Bayesian Optimization for Data-Driven Design of Arbitrary Controllers with Gaussian Process Emulators.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021
On the Stability Properties of Perception-aware Chance-constrained MPC in Uncertain Environments.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021
Deep Learning-based Approximate Nonlinear Model Predictive Control with Offset-free Tracking for Embedded Applications.
Proceedings of the 2021 American Control Conference, 2021
Perception-Aware Chance-Constrained Model Predictive Control for Uncertain Environments.
Proceedings of the 2021 American Control Conference, 2021
2020
Int. J. Control, 2020
Approximate Closed-Loop Robust Model Predictive Control With Guaranteed Stability and Constraint Satisfaction.
IEEE Control. Syst. Lett., 2020
A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization.
CoRR, 2020
An internal model control design method for failure-tolerant control with multiple objectives.
Comput. Chem. Eng., 2020
Surrogate modeling for fast uncertainty quantification: Application to 2D population balance models.
Comput. Chem. Eng., 2020
Stability analysis and stabilization of discrete-time non-homogeneous semi-Markov jump linear systems: A polytopic approach.
Autom., 2020
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020
Proceedings of the 59th IEEE Conference on Decision and Control, 2020
Proceedings of the 59th IEEE Conference on Decision and Control, 2020
Safe Learning-based Model Predictive Control under State- and Input-dependent Uncertainty using Scenario Trees.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020
2019
Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions.
PLoS Comput. Biol., 2019
Model predictive control with active learning for stochastic systems with structural model uncertainty: Online model discrimination.
Comput. Chem. Eng., 2019
A Constraint-Tightening Approach to Nonlinear Model Predictive Control with Chance Constraints for Stochastic Systems.
Proceedings of the 2019 American Control Conference, 2019
Proceedings of the 2019 American Control Conference, 2019
2018
Stochastic model predictive control with active uncertainty learning: A Survey on dual control.
Annu. Rev. Control., 2018
Shaping the Closed-Loop Behavior of Nonlinear Systems Under Probabilistic Uncertainty Using Arbitrary Polynomial Chaos.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018
Stochastic Model Predictive Control with Enlarged Domain of Attraction for Offset-Free Tracking.
Proceedings of the 2018 Annual American Control Conference, 2018
Proceedings of the 2018 Annual American Control Conference, 2018
2017
A probabilistic framework for reference design for guaranteed fault diagnosis under closed-loop control.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017
2016
Proceedings of the 55th IEEE Conference on Decision and Control, 2016
Model predictive control of thermal effects of an atmospheric pressure plasma jet for biomedical applications.
Proceedings of the 2016 American Control Conference, 2016
Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions.
Proceedings of the 2016 American Control Conference, 2016
A polynomial chaos-based nonlinear Bayesian approach for estimating state and parameter probability distribution functions.
Proceedings of the 2016 American Control Conference, 2016
2015
Int. J. Control, 2015
Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints.
CoRR, 2015
Lyapunov-based Stochastic Nonlinear Model Predictive Control: Shaping the State Probability Density Functions.
CoRR, 2015
Proceedings of the American Control Conference, 2015
Proceedings of the American Control Conference, 2015
2014
Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints.
CoRR, 2014
A Probabilistic Approach to Robust Optimal Experiment Design with Chance Constraints.
CoRR, 2014
Stability for Receding-horizon Stochastic Model Predictive Control with Chance Constraints.
CoRR, 2014
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014
Proceedings of the American Control Conference, 2014
2013
Proceedings of the 12th European Control Conference, 2013
Proceedings of the 12th European Control Conference, 2013
Proceedings of the 12th European Control Conference, 2013
Design of multi-objective failure-tolerant control systems for infinite-dimensional systems.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013
2012
Nonlinear Model-Based Control of a Semi-Industrial Batch Crystallizer Using a Population Balance Modeling Framework.
IEEE Trans. Control. Syst. Technol., 2012
A unified experiment design framework for detection and identification in closed-loop performance diagnosis.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012
Proceedings of the 51th IEEE Conference on Decision and Control, 2012
Proceedings of the American Control Conference, 2012
2011
Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011
2009