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Deep learning of vehicle dynamics 20

Deep learning of vehicle dynamics

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20th ifac symposium on System Identification (SYSID)

作者： Szecsi, M. Gyorok, B. Weinhardt-Kovacs, A. Beintema, G. I. Schoukens, M. Peni, T. Toth, R. Eindhoven Univ Technol Control Syst Grp Eindhoven Netherlands HUN REN Inst Comp Sci & Control Syst & Control Lab Budapest Hungary

Recent advances in deep-learning-based identification of dynamic systems have resulted in a new generation of approaches utilizing state-space neural models with innovation noise structure, improved reformulation of multiple shooting, batch optimization, and a subspace identification-inspired form of encoders. The latter is used to learn a reconstructability map to estimate model states from past inputs and outputs. By using the SUBNET approach, which belongs to the state-of-the-art of these methods, we show how to effectively use these approaches to identify reliable vehicle models from data both in continuous and discrete time, respectively. We showcase the approach on the identification of the dynamics of a Crazyflie 2.1 nano-quadcopter and an Fltenth electric car both in a high-fidelity simulation environment, and in case of the electric car, on real measured data. The results indicate that new-generation of deep-learning methods offer efficient system identification of vehicle dynamics in practice. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Nonlinear system identification Deep learning Automotive system identification and modelling UAVs

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Set Membership identification for NMPC complexity reduction 20

Set Membership identification for NMPC complexity reduction

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20th ifac symposium on System Identification (SYSID)

作者： Boggio, Mattia Novara, Carlo Taragna, Michele Politecn Torino Dept Elect & Telecommun Corso Duca Abruzzi 24 I-10129 Turin Italy

A Set Membership (SM) approach is proposed to reduce the computational burden of Nonlinear Model Predictive control (NMPC) algorithms. In particular, a SM identification method is applied to derive an approximation and tight bounds of the NMPC control law, using a set of its values computed offline. These quantities are used online to reduce the dimension and the volume of the search domain of the NMPC optimization algorithm, and to perform a warm start, allowing a significant shortening of the computational time. The developed NMPC methodology is tested in simulation, considering an obstacle avoidance application in a realistic autonomous vehicle scenario. The obtained results demonstrate the effectiveness of the proposed approach in terms of computation time, without affecting the solution quality. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Set Membership identification Nonlinear Model Predictive control complexity reduction data-aided control autonomous vehicles

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12th ifac symposium on Advanced control of Chemical Processes, ADCHEM 2024 - Proceedings

12th IFAC Symposium on Advanced Control of Chemical Processe...

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12th ifac symposium on Advanced control of Chemical Processes, ADCHEM 2024

The proceedings contain 148 papers. The topics discussed include: discovering latent causal variables using a trade-off between compression and causality;enhanced hybrid model for gas-lifted oil production;human-in-the-loop controller tuning using preferential Bayesian optimization;interpretable propagation path neural network for fault detection and diagnosis;machine learning multi-step-ahead modelling with uncertainty assessment;fault data injection detection on a digital-twin: Fresnel solar concentrator;two-stage stochastic scheduling of a multiproduct pipeline system using similarity index decomposition;model-based design of the temperature controller of a shrink tunnel;smart monitoring and predictive maintenance for an offshore natural gas dehydration unit;a simplified dynamic model of direct stem generation solar plants for state estimation and control applications;and optimizing crude oil operations scheduling considering blending in tanks.

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Optimizing crude oil operations scheduling considering blending in tanks 12

Optimizing crude oil operations scheduling considering blend...

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12th ifac symposium on Advanced control of Chemical Processes (ADCHEM)

作者： Garcia-Verdier, Tomas Garcia Gutierrez, Gloria Mendez, Carlos de Prada, Cesar Univ Valladolid Dept Syst Engn & Automat Control Valladolid Spain Univ Valladolid Inst Sustainable Proc Valladolid Spain UNL CONICET INTEC CAPSE Buenos Aires DF Argentina

This paper focuses on the optimization of crude oil operations scheduling in a refinery that is supplied with crude oil by ship. One of the main challenges associated with the crude oil operations scheduling problem is the management of crude storage in tanks. Since storage capacity is limited and there are several types of crude oil depending on their composition, it is necessary to store mixtures of crude oil in tanks. This feature makes necessary the inclusion of nonlinear, non-convex constraints, which complicates the resolution of mathematical programming models. To address this problem, we have developed a mathematical programming model based on a continuous-time formulation using time slots, along with a strategy based on piecewise McCormick relaxation that allows us to efficiently handle the nonlinear constraints generated by blending crude oils in tanks. Copyright (C)2024 The Authors. This is an open access article under the CC BY-NC-ND license (htips://***/licenses/by-nc-nd/4.0/)

关键词： modelling and decision making in complex systems Efficient strategies for large scale complex systems Production planning and control Optimization and control of large-scale network systems Complex logistic systems

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Regularized Finite Impulse Response Models versus Laguerre Models: A Comparison 20

Regularized Finite Impulse Response Models versus Laguerre M...

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20th ifac symposium on System Identification (SYSID)

作者： Illg, Christopher Nelles, Oliver Univ Siegen Automat Control Mechatron Siegen Germany

While in recent years, the estimation of finite impulse response (FIR) models has been improved by introducing new regularization schemes, also other orthonormal basis function (OBF) models are now becoming more prominent again. Although Laguerre models show very similar properties to the regularized FIR models, they are only rarely used for system identification. Therefore, in this paper, regularized FIR models and Laguerre models will be compared. This work focuses on the model structure and its similarities and differences, as well as the hyperparameter optimization utilizing the generalized cross-validation (GCV) error. Finally, the two model types are investigated using three different processes and the model performance is evaluated. Both model types show significant improvements compared to standard (unregularized) FIR models. Copyright (c) 2024 The Authors.

关键词： Regularized FIR Models Laguerre Models System Identification Hyperparameter Optimization

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A switched system for modeling the +-inal dilation exercises 12

A switched system for modeling the +-inal dilation exercises

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12th ifac symposium on Biological and Medical systems, BMS 2024

作者： Jackson, Roxanne R. Varagnolo, Damiano Dewitte, Marieke Knorn, Steffi Department of Control Engineering Technische Universität Berlin Germany Department of Engineering Cybernetics Norwegian University of Science and Technology Norway Department of Experimental Health Psychology Maastricht University Netherlands

Genito-pelvic pain/penetration disorders, i.e., painful experiences during penetrative sexual intercourse, affect an estimated 30-40% of people with vaginas at some point in their lives. Treatments for Genito-pelvic pain/penetration disorders vary depending on the main cause of the condition but may include the use of vaginal trainers/dilators which are used to stretch the vaginal duct and relax the Pelvic floor muscles. As a step towards understanding how patients respond to vaginal trainers, we investigate the interaction of pain and pleasure characteristics during treatments. In this paper, we adapt an existing qualitative model into a switched system where switches are determined by a patient's subjective level of pleasure. A qualitative model of the pain and fear characteristics is derived for each switched state. Due to sparsity in the experimental data, we adopt a weighted least squares approach for parameter identification using online (constant sampling period) and offline data (sparse and inconsistent sampling). The results indicate that it is a promising method compared to the grey-box model without considering pleasure levels but there is an obvious and variable delay in the reporting of a patient's pleasure levels which affects the overall performance of the switched system. © 2024 The Authors. This is an open access article under the CC BY-NC-ND license.

关键词： Decision support systems

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Frequency-Domain Identification of Discrete-Time systems using Sum-of-Rational Optimization 20

Frequency-Domain Identification of Discrete-Time Systems usi...

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20th ifac symposium on System Identification (SYSID)

作者： Abdalmoaty, Mohamed Miller, Jared Yin, Mingzhou Smith, Roy S. Swiss Fed Inst Technol Automat Control Lab IfA Phys Str 3 CH-8092 Zurich Switzerland

This paper proposes a new computationally tractable method to fit coefficients of a fixed-order discrete-time transfer function to a measured frequency response, with stability guaranteed. The problem is formulated as a non-convex global sum-of-rational optimization problem whose objective function is the sum of weighted squared residuals at each observed frequency datapoint. Stability is enforced using a polynomial matrix inequality constraint. The problem is solved by a moment-sum-of-squares hierarchy of semidefinite programs through a framework for sum-of-rational-functions optimization. Convergence of the moment-sum-of-squares program is guaranteed as the bound on the degree of the sum-of-squares polynomials approaches infinity. The performance of the proposed method is demonstrated using numerical simulation examples. Copyright (c) 2024 The Authors.

关键词： Frequency domain identification System analysis and optimization Linear systems Large scale optimization problems Rational optimization Convex optimization

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Model-based dynamical voltage prediction of Zinc-Air cell for piecewise discharge currents 12

Model-based dynamical voltage prediction of Zinc-Air cell fo...

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12th ifac symposium on Advanced control of Chemical Processes (ADCHEM)

作者： Pineda-Rodriguez, Juan Diego Vlad, Cristina Rodriguez-Ayerbe, Pedro Lao-atiman, Woranunt Olaru, Sorin Kheawhom, Soorathep Univ Paris Saclay Cent Supelec CNRS Lab Signals & Syst F-91190 Gif Sur Yvette France Chulalongkorn Univ Fac Engn Dept Chem Engn Bangkok 10330 Thailand

This paper examines the construction of a parameter-dependent voltage prediction model for a primary Zinc-air cell prototype, focusing on its response time when subjected to multiple step-wise discharge current levels. Laboratory tests have revealed that the dynamic response's time constant varies with discharge current, a phenomenon not adequately addressed in previous analyses. The current research aims to contribute to the existing knowledge by employing a piecewise current profile during a single cell discharge and conducting an identification-type analysis of the relationships between the system's time constants and other state and input variables. The findings presented in this paper hold significant potential for integration into Battery Management systems and, in the long term, for addressing the inverse problem of State-of-Charge estimation. Copyright (C) 2024 The Authors.

关键词： Parameter identification Energy storage systems Battery modelling Zinc-air cell

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Data-driven explicit predictive control with limited resources: an exploration-based strategy 20

Data-driven explicit predictive control with limited resourc...

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20th ifac symposium on System Identification (SYSID)

作者： Sassella, Andrea Breschi, Valentina Formentin, Simone Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands

Retrieving optimal control actions in a receding horizon fashion at run time might be a challenging task, especially when the sampling time of the system to be controlled is small and the optimization problem is large. Although explicit solutions have been proposed to tackle this challenge, the complexity of the explicit control law scales poorly with the dimension of the problem. In the attempt to cope with these limitations within the challenging data-driven setup, we propose to construct a limited-complexity approximation of the explicit predictive law by iteratively exploring the state/reference space while leveraging structural priors on the input parameterization. The same approximation can be exploited to compute the control action also when the closed-loop system visits unexplored regions. The performance of the proposed strategy is assessed on a simple numerical example. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Data-driven control

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Regularized Iterative Weighted Total Least Squares for Vehicle Mass Estimation 20

Regularized Iterative Weighted Total Least Squares for Vehic...

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20th ifac symposium on System Identification (SYSID)

作者： Koide, Hugo Vayssettes, Jeremy Mercere, Guillaume Poitiers Univ Lab Comp Sci & Automat Control Syst Poitiers France Michelin Res & Dev Ctr F-63118 Cebazal France

This work addresses the challenge of vehicle mass estimation using a longitudinal vehicle dynamics model, which adopts an errors-in-variables formulation due to the presence of noise-contaminated measurements in both input and output variables. The reduced vehicle dynamics model is ill-conditioned by nature of correlated input variables and a lack of persistent excitation in the measured data. A regularized iterative weighted total least squares (RIWTLS) method is therefore developed and has the advantage of producing parameter uncertainty quantification and measurement bias estimation alongside the estimated system parameters. A complementary adaptive regularization scheme is developed and serves to control the numerical stability of the RIWTLS algorithm based on the conditioning of incoming data. Experimental tests using electric vehicle data and a batch estimation scheme highlight the performance of the proposed RIWTLS algorithm, estimating vehicle mass to within +/- 1% accuracy. Copyright (c) 2024 The Authors.

关键词： Vehicle mass estimation Errors-in-variables Weighted total least squares Uncertainty quantification

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