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- Kergus, Pauline, et al.
(författare)
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Learning-based hierarchical control of water reservoir systems
- 2022
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Ingår i: IFAC Journal of Systems and Control. - : Elsevier BV. - 2468-6018. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- The optimal control of a water reservoir system represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives. In this work, we propose a real-time learning-based control strategy based on a hierarchical predictive control architecture. Two control loops are implemented: the inner loop is aimed to make the overall dynamics similar to an assigned linear model through data-driven control design, then the outer economic model-predictive controller compensates for model mismatches, enforces suitable constraints, and boosts the tracking performance. The effectiveness of the proposed approach is illustrated on an accurate simulator of the Hoa Binh reservoir in Vietnam. Results show that the proposed approach outperforms stochastic dynamic programming.
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- Knorn, Steffi, et al.
(författare)
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Data-driven models of pelvic floor muscles dynamics subject to psychological and physiological stimuli
- 2019
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Ingår i: IFAC Journal of Systems and Control. - : Elsevier BV. - 2468-6018. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes individualized, dynamical and data-driven models to describe pelvic floor muscle responses in women undergoing vaginal dilation. Specifically, the models describe how the aggregated pressure exerted by the pelvic floor muscles of women change due to physiological and psychological stimuli. Specifically, women experienced inflation of a balloon at the vaginal introitus while watching different short movies such as with or without sexual content. The paper inspects the approximation capabilities of different model structures, such as Hammerstein–Wiener and NARX, for this specific application, and finds the specific model structures and orders that best describe the recorded measurement data. Moreover, the manuscript explores the trade-offs between individualization and averaging of models. More precisely it numerically assesses how models obtained by assuming that each individual has the same response can be used to simulate the responses of different patients. Although the current dataset is drawn from a sample of healthy volunteers, this paper is an initial step towards better understanding women’s responses to vaginal dilation and sexual/nonsexual videos and facilitating individualized medical vaginal dilation treatment.
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| 4. |
- Tufvesson, Pex, et al.
(författare)
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Automatic control of reactive brain computer interfaces
- 2024
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Ingår i: IFAC Journal of Systems and Control. - 2468-6018. ; 27:BMS
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Tidskriftsartikel (refereegranskat)abstract
- This article discusses theoretical and practical aspects of real-time brain computer interface control methods based on Bayesian statistics. The theoretical aspects include how the data from the brain computer interface can be translated into a Gaussian mixture model that is used in the Bayesian statistics-based control methods. The practical aspects include how the control methods improve the performance of the brain computer interface. We use a reactive brain computer interface based on a visual oddball paradigm for the investigation and improvement of the performance of automatic control and feedback algorithms used in the system. By using automatic control for selection of the stimuli for the visual oddball experiment, the target stimulus is identified faster than if no automatic control is used. Finally, we introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.
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- Wahlquist, Ylva, et al.
(författare)
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Automated covariate modeling using efficient simulation of pharmacokinetics
- 2024
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Ingår i: IFAC Journal of Systems and Control. - 2468-6018. ; 27
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Tidskriftsartikel (refereegranskat)abstract
- Pharmacometric modeling plays an important role in drug development and personalized medicine. Pharmacometric covariate models can be used to describe the relationships between patient characteristics (such as age and weight) and pharmacokinetic (PK) parameters. Traditionally, the functional structure of these relationships are obtained manually. This is a time-consuming task, and consequently limits the search space of covariate relationships. The use of data-driven machine learning (ML) in pharmacometrics has the potential to automate the search for adequate model structures, which can speed up the modeling process and enable the evaluation of a wider range of model candidates. Even with moderately sized data sets, ML approaches require millions of simulations of pharmacokinetic (PK) models, which dictates the need for an efficient simulator. In this paper, we demonstrate how to automate covariate modeling using neural networks (NNs), that are trained using efficient PK simulation techniques. We apply the methodology to a propofol data set with 1031 individuals and compare the results to previously published covariate models for propofol. We use the NN as a function approximator that relates covariates to the parameters of a three-compartment PK model, and train it on dose and plasma concentration time series. Our study demonstrates that NN-based covariate modeling allows for automation of the otherwise time-consuming task of identifying which of available covariatesto include in the model, and what functional mappings from these covariates to PK model parameters to consider in the model search. Additional to this saving in modeller effort, the NN-based model obtained in our clinical data set example has PK parameters within a clinically reasonable range, and slightly enhanced predictive precision than a previously published state-of-the-art covariate models for propofol model.
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