111,888 research outputs found
Direct data-driven design of switching controllers
No full textSwitching linear models can be used to represent the behavior of hybrid, time-varying, and nonlinear systems, while generally providing a satisfactory trade-off between accuracy and complexity. Although several control design techniques are available for such models, the effect of modeling errors on the closed-loop performance has not been formally evaluated yet. In this paper, a data-driven synthesis scheme is thus introduced to design optimal switching controllers directly from data, without needing a model of the plant. In particular, the theory will be developed for piecewise affine controllers, which have proven to be effective in many real-world engineering applications. The performance of the proposed approach is illustrated on some benchmark simulation case studies
Proper closed-loop specifications for data-driven model-reference control
No full textIn control applications where finding a model of the plant is costly and time consuming, direct data-driven approaches represent a valid alternative for the design of model reference controllers. However, the selection of a proper reference model within a model-free setting is known to be a critical task, as such a model typically plays the role of a hyperparameter. In this work, we extend the existing theory so as to compute both a reference model and the corresponding optimal controller parameters from data to satisfy given behavioral bounds on the desired closed-loop performance. The effectiveness of the proposed approach is illustrated 011 a benchmark simulation example
"Filologia italiana"
No full textSaggi teorici e storico-filologici sul problema della vulgata di testi medievali e rinascimentali
(finito di stampare: maggio 2007)
Data-driven design of explicit predictive controllers with structural priors
No full textIn this letter, we propose a data-driven approach to derive explicit predictive control laws. The key idea of the presented strategy is to exploit the prior knowledge that the optimal solution is a piece-wise affine controller. As the proposed method allows us to automatically retrieve also a model of the closed-loop system, we show that we can apply classical Lyapunov techniques to perform a prior stability check for safe controller deployment. The effectiveness of the proposed strategy is assessed on a benchmark simulation example, through which we also discuss the use of regularization and preprocessing techniques to handle the presence of noise.</p
Francesco Galeota, Le lettere del Colibeto, edizione, spoglio linguistico e glossario a cura di V. F
No full textDirect data-driven design of switching controllers for constrained systems
No full textThis paper presents a hierarchical structure to directly design controllers for (possibly nonlinear) constrained systems. The proposed architecture combines the advantages of an inner data-driven switching controller designed to achieve a predefined closed-loop behavior and an outer model predictive controller, which is used as a reference governor. These design choices enable us to avoid the identification step typical of model-based approaches while exploiting the ability of model predictive controllers to handle constraints and optimize the closed-loop performance. As a proof of concept, a benchmark simulation example is used to demonstrate the effectiveness of the proposed strategy
Piecewise nonlinear regression with data augmentation
No full textPiecewise regression represents a powerful tool to derive accurate yet modular models describing complex phenomena or physical systems. This paper presents an approach for learning PieceWise NonLinear (PWNL) functions in both a supervised and semi-supervised setting. We further equip the proposed technique with a method for the automatic generation of additional unsupervised data, which are leveraged to improve the overall accuracy of the estimate. The performance of the proposed approach is preliminarily assessed on two simple simulation examples, where we show the benefits of using nonlinear local models and artificially generated unsupervised data
On data-driven design of LPV controllers with flexible reference models
No full textMany data-driven control design methods require the a-priori selection of a reference model to be tracked. In case of limited priors on the plant, such a blind choice might ultimately compromise the overall performance. In this work, we propose a nested strategy for the direct design of Linear Parameter Varying (LPV) controllers from data, in which the reference model is treated as a hyperparameter to be tuned. The proposed approach allows one to jointly optimize the reference model and learn an LPV controller, solely based on soft specifications on the desired closed-loop. The effectiveness of the proposed technique is assessed on a benchmark case study, with the obtained results showing its potential advantages over a state-of-the-art method
Uncertainty-aware data-driven predictive control in a stochastic setting
No full textData-Driven Predictive Control (DDPC) has been recently proposed as an
effective alternative to traditional Model Predictive Control (MPC), in that
the same constrained optimization problem can be addressed without the need to
explicitly identify a full model of the plant. However, DDPC is built upon
input/output trajectories. Therefore, the finite sample effect of stochastic
data, due to, e.g., measurement noise, may have a detrimental impact on
closed-loop performance. Exploiting a formal statistical analysis of the
prediction error, in this paper we propose the first systematic approach to
deal with uncertainty due to finite sample effects. To this end, we introduce
two regularization strategies for which, differently from existing
regularization-based DDPC techniques, we propose a tuning rationale allowing us
to select the regularization hyper-parameters before closing the loop and
without additional experiments. Simulation results confirm the potential of the
proposed strategy when closing the loop.Comment: 6 pages, 1 figure, this work has been submitted and accepted for
publication at the IFAC World Congress 2023, Yokohama, Japa
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