Machine-learning-based state estimation and predictive control of nonlinear processes

Alhajeri, Mohammed S.; Wu, Zhe; Rincón, David; Albalawi, Fahad; Christofides, Panagiotis D.

doi:10.1016/j.cherd.2021.01.009

Cited by 37 publications

(12 citation statements)

References 33 publications

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“…Although state-space representations can be obtained from ML black-box models [100], they may also be constructed based on first-principle knowledge of the system dynamics, as with Kalman filters. MLbased state-space models which use both data and firstprinciples are found in the literature [288][289][290], but the authors did not find applications in vibroacoustic.…”

Section: System Identificationmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

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The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD&V and guides the reader to an advanced understanding of progress and prospects in the field.

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Section: System Identificationmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Most research has focused on an automatic data-based adaptation of the prediction model or uncertainty description. The feedback techniques have the ability to overcome and reduce the impact of uncertainty [59]. The LB-MPC embeds the ML method in the MPC framework to eradicate the influence of uncertain disturbances, thus improving the performance of path tracking in mobile platforms [26,60].…”

Section: Learning-based On Model Predictive Controlmentioning

confidence: 99%

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

et al. 2022

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The learning-based model predictive control (LB-MPC) is an effective and critical method to solve the path tracking problem in mobile platforms under uncertain disturbances. It is well known that the machine learning (ML) methods use the historical and real-time measurement data to build data-driven prediction models. The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. The LB-MPC combines the advantages of ML and MPC. In this work, the LB-MPC technique is summarized, and the application of path tracking control in mobile platforms is discussed by considering three aspects, namely, learning and optimizing the prediction model, the controller design, and the controller output under uncertain disturbances. Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed.

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“…The converged states are then considered to be the initial states of the identified model for prediction purposes. [ 48,49 ] However, in this work, to assess the performance of the hybrid approach on the unseen data, a portion of the validation data (last 1000 validation data samples), in addition to the testing data, is fed to the model in order to ensure the output convergence (convergence must take place while using the validation data samples, which are available in advance). Remark Note that the hybrid approach is developed for a multi‐zone building where the different zones follow a similar dynamic behaviour, albeit with different parameters (internal and external loads). In this situation, when a well‐trained data‐driven model of a representative zone is available (like the pre‐trained RNN model in this work), it can be used to generate the residual.…”

Section: Application Of the Hybrid Approach To The Multi‐zone Fitness...mentioning

confidence: 99%

A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling HVAC systems

2022

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This paper addresses the problem of system identification for heating, ventilation, and air conditioning (HVAC) systems using a relatively small amount of data for the zone under consideration, by leveraging larger datasets for similar zones. To this end, a hybrid machine learning approach is developed where a pre‐trained recurrent neural network (RNN) model, trained on a large amount of data from a representative zone, is leveraged to build models for the other zones using a smaller amount of data. This is achieved by developing a hybrid model that integrates the pre‐trained RNN model with the models built using the subspace identification (SubID) technique to predict the residuals (differences between the real outputs and the predicted outputs from the pre‐trained RNN model) in the other zones. The effectiveness of the proposed hybrid approach is shown using real data collected from a multi‐zone fitness centre. The results demonstrate the superior performance of the hybrid approach over the cases where individual RNN and SubID models are directly developed using only the data from the zones in question.

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Machine-learning-based state estimation and predictive control of nonlinear processes

Cited by 37 publications

References 33 publications

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling HVAC systems

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