Stochastic Approach for System Identification using Machine Learning

Abdufattokhov, Shokhjakhon; Muhiddinov, Behzod

doi:10.1109/dynamics47113.2019.8944452

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Due to the possibility to include prior knowledge making the method more attractive as compared to other regression algorithms, GP models have been employed in different research fields [24]- [26]. This section provides the necessary background about GP and framework to build a probabilistic and predictive model for regression problems mainly, adopted from [27], [28].…”

Section: Learning Building Model With Gaussian Processesmentioning

confidence: 99%

Predictive and probabilistic modelling using machine learning for building indoor climate control

Abdufattokhov

Mahamatov

Ibragimova

et al. 2022

IJEECS

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For the last few decades, thermal comfort has been considered an aspect of sustainable building evaluation methods and tools. However, estimating the indoor air temperature of buildings is a complicated task due to the nonlinear behaviour of heating, ventilation and air conditioning systems combined with complex dynamics characterized by the time-varying environment with disturbances. This issue can be alleviated by modelling the building dynamics using Gaussian processes since it also measures the uncertainty bounds. The main focus of this paper is designing a predictive and probabilistic room temperature model of buildings using Gaussian processes and incorporating it into model predictive control to minimize energy consumption and provide thermal comfort satisfaction. We exploited the Gaussian processes’ full probabilistic capabilities as the mean prediction for the room temperature model and used the model uncertainty in the objective function not to lose the desired performance and to design a robust control scheme. We illustrated the potentials of the proposed method in a numerical example with simulation results.

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Section: Learning Building Model With Gaussian Processesmentioning

confidence: 99%

Predictive and probabilistic modelling using machine learning for building indoor climate control

Abdufattokhov

Mahamatov

Ibragimova

et al. 2022

IJEECS

Self Cite

View full text Add to dashboard Cite

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“…Another data-driven identification solution can be found in [18], which can ease the tuning of the hyperparameters of the model identification process. A non-parametric and probabilistic approach is proposed by [19] for identifying nonlinear system considering uncertain noises on the measured signals. particle Bernstein polynomials-based regression method is presented in [20], which is suitable for multivariate regression problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles

2021

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This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (LPV) structure. The scheduling variables of the LPV model through machine-learning-based methods using a big dataset are selected. Moreover, the LPV model parameters through an optimization algorithm are computed, with which accurate fitting on the dataset is achieved. The proposed method is illustrated on the nonlinear modeling of the lateral vehicle dynamics. The resulting LPV-based vehicle model is used for the control design of path following functionality of autonomous vehicles. The effectiveness of the modeling and control design methods through comprehensive simulation examples based on a high-fidelity simulation software are illustrated.

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