Welcome
I am a researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), Lugano, Switzerland. My research work is focused on
- Data-driven modelling and control of non-linear and hybrid dynamical systems.
- Application of machine learning and AI algorithms to problems pan-industry.
Download my PhD thesis.
Interests
- Artificial Intelligence
- Control Theory
- Hybrid Systems
- Machine Learning
- Model Predictive Control
- System Identification
Education
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Ph.D. in Control Systems, 2018
IMT School for Advanced Studies Lucca, Italy.
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M.Tech. in Control Systems, 2013
University of Mumbai, India.
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B.E. in Electronics & Telecomm, 2011
University of Mumbai, India.
Experience
Researcher
Learning for Decision and Control, Imprecise Probability group, IDSIA
- Group lead: Dr. Dario Piga
- Topics: Machine Learning, System Identification, Automatic Control, Artificial Intelligence
Postdoctoral Research Fellow
University of Lille, Ecole Centrale Lille
- Advisor: Dr. Mihaly Petreczky
- Topic: Realization and identification algorithms for stochastic linear parameter-varying models
Visiting Researcher
Control Systems group, Eindhoven University of Technology (TU/e)
- Hosted by: Dr. Roland Toth
- Topic: Kernel methods for LPV system identification
PhD student
IMT School for Advanced Studies Lucca
Senior Research Fellow
Centre of Excellence in Complex and Non-linear Dyanmical Systems, VJTI
- Advisors: Prof. Faruk Kazi, Prof. N. M. Singh
- Projects:
- A system theoretic approach for modeling complex process (Collaboration: Board of Research in Nuclear Sciences (BRNS), BARC Mumbai)
- Non-linear control strategies for energy management in hybrid power sources
Journal Articles
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(2024).
Parameter dependent robust control invariant sets for LPV systems with bounded parameter variation rate.
IEEE Transactions on Automatic Control.
(2024).
Shedding light on the ageing of extra virgin olive oil: Probing the impact of temperature with fluorescence spectroscopy and machine learning techniques.
LWT Food Science and Technology.
(2023).
Data-Driven Synthesis of Configuration-Constrained Robust Invariant Sets for Linear Parameter-Varying Systems.
IEEE Control System Letters and American Control Conference 2024.
(2023).
Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems.
IEEE Control System Letters.
(2023).
Computation of Parameter Dependent Robust Invariant Sets for LPV Models with Guaranteed Performance.
Automatica.
(2022).
Direct identification of continuous-time LPV state-space models via an integral architecture.
Automatica.
(2022).
Maximum—A-Posteriori Estimation of Linear Time-Invariant State-Space Models via Efficient Monte-Carlo Sampling.
ASME Letters in Dynamic System and Control.
(2021).
Learning dynamical systems from quantized observations: a Bayesian perspective.
IEEE Transactions on Automatic Control.
(2020).
Identification of hybrid and linear parameter‐varying models via piecewise affine regression using mixed integer programming.
International Journal of Robust and Nonlinear Control.
(2020).
Recursive Bias-Correction Method for Identification of Piecewise Affine Output-Error Models.
IEEE Control Systems Letters.
(2018).
A bias-correction method for closed-loop identification of Linear Parameter-Varying systems.
Automatica.
(2017).
Improving Lifetime of Fuel Cell in Hybrid Energy Management System by Lure–Lyapunov-Based Control Formulation.
IEEE Transactions on Industrial Electronics.
Conference Proceedings
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(2023).
Direct Data-Driven Computation of Polytopic Robust Control Invariant Sets and State-Feedback Controllers.
IEEE Conference on Decision and Control (CDC'23).
(2023).
Direct identification of continuous-time linear switched state-space models.
IFAC World Congress (IFAC'23).
(2023).
Variational autoencoder for the identification of piecewise models.
IFAC World Congress (IFAC'23).
(2022).
Data-driven statistical analysis for discharge position prediction on Wire EDM.
21st CIRP Conf on Electro Physical and Chemical Machining.
(2021).
An integral architecture for identification of continuous-time state-space LPV models.
IFAC Workshop on Linear Parameter-Varying Systems (LPVS'21).
(2021).
Deep learning with transfer functions: new applications in system identification.
IFAC Symposium on System Identification (SYSID'21).
(2020).
Shrinkage Strategies for Structure Selection and Identification of Piecewise Affine Models.
IEEE Conference on Decision and Control (CDC'20).
(2020).
A Bias-Correction Approach for the Identification of Piecewise Affine Output-Error Models.
IFAC World Congress (IFAC'20).
(2019).
Consistent and computationally efficient estimation for stochastic LPV state-space models: realization based approach.
IEEE Conference on Decision and Control (CDC'19).
(2019).
Realization and identification algorithm for stochastic LPV state-space models with exogenous inputs.
IFAC Workshop on Linear Parameter-Varying Systems (LPVS'19).
(2019).
Kernelized Identification of Linear Parameter-Varying Models with Linear Fractional Representation.
European Control Conference (ECC'19).
(2018).
Energy Disaggregation using Piecewise Affine Regression and Binary Quadratic Programming.
IEEE Conference on Decision and Control (CDC'18).
(2018).
Regularized Moving-Horizon PWA Regression for LPV System Identification.
IFAC Symposium on System Identification (SYSID'18).
(2017).
LPV Model Order Selection from Noise-corrupted Output and Scheduling Signal Measurements.
20th IFAC World Congress (IFAC'17).
(2017).
Regularized moving-horizon piecewise affine regression using mixed-integer quadratic programming.
Mediterranean Conference on Control and Automation (MED'17).
(2016).
Regularized least square support vector machines for order and structure selection of LPV-ARX models.
European Control Conference (ECC'16).
Preprints
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(2024).
Direct Data-Driven Design of LPV Controllers and Polytopic Invariant Sets with Cross-Covariance Noise Bounds.
Technical Report, August 2024.
(2022).
Variational autoencoder for the identification of piecewise models.
accepted IFAC World Congress 2023, Yokohama, Japan.
(2022).
Learning neural state-space models: do we need a state estimator?.
preprint.
Contact
- manas.mejari@supsi.ch
- Via la Santa 1, Lugano-Viganello, 6962