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EXPLOITING EQUIVARIANCE IN THE DESIGN OF TRACKING controlLERS FOR EULER-POINCARE systems ON MATRIX LIE GROUPS

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arXiv 2024年

作者： Hampsey, Matthew Banavar, Ravi van Goor, Pieter Mahony, Robert Systems Theory and Robotics Group Australian National University ACT2601 Australia Interdisciplinary Programme in Systems and Control Engineering Indian Institute of Technology Bombay India

The trajectory tracking problem is a fundamental control task in the study of mechanical systems. A key construction in tracking control is the error or difference between an actual and desired trajectory. This construction also lies at the heart of observer design and recent advances in the study of equivariant systems have provided a template for global error construction that exploits the symmetry structure of a group action if such a structure exists. Hamiltonian systems are posed on the cotangent bundle of configuration space of a mechanical system and symmetries for the full cotangent bundle are not commonly used in geometric control theory. In this paper, we propose a group structure on the cotangent bundle of a Lie-group and leverage this to define momentum and configuration errors for trajectory tracking drawing on recent work on equivariant observer design. We show that this error definition leads to error dynamics that are themselves "Euler-Poincare like" and use these to derive simple, almost global trajectory tracking control for fully-actuated Euler-Poincare systems on a Lie-group state space. Copyright © 2024, The Authors. All rights reserved.

关键词： Lie groups

A Review of Parameterised MPC Algorithms

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IFAC-PapersOnLine 2023年第2期56卷 7692-7697页

作者： John A. Rossiter Oscar J. Gonzalez-Villareal Mohammad A.M. Sarbini Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Electrical and Electronic Engineering Programme Area Universiti Teknologi Brunei

This paper provides an evaluation and comparison of popular parameterised model predictive control approaches that have been proposed in the literature in recent years. Using the Generalised Predictive control (GPC) algorithm as the baseline algorithm, the paper sets out a number of performance criteria to compare and contrast with several other MPC approaches. Numerical examples use 100 random samples of 2, 3, and 4-state models and the approaches are compared using the selected performance criteria.

关键词： Predictive control Model predictive and optimization-based control Monte Carlo methods

Hybrid Modelling for the Multi-Criteria Decision Making of Energy systems: An Application for Geothermal District Heating System

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SSRN

SSRN 2023年

作者： Arslan, Asli Ergenekon Arslan, Oguz Genc, M. Serdar Quality Control in Manufacturing Programme Vocational School Bilecik Seyh Edebali University Bilecik Turkey Mechanical Engineering Department Engineering Faculty Bilecik Seyh Edebali University Bilecik Turkey Energy Systems Engineering Department Engineering Faculty Erciyes University Kayseri Turkey

It is a very vital issue to evaluate the criteria affecting the processes in the optimization of the energy systems. The multi-criteria decision-making analysis plays an essential role in this regard. The efficiency analysis technique with output satisficing (EATWOS) is a successful tool for determining the most efficient design for energy systems. Since EATWOS is rationally based on the maximal output throughout the minimal inputs, the weights of input and output values considerably affect the analysis results. Therefore, the impact ratio of each input and output term should be sensitively determined. In this study, the artificial neural network (ANN) modelling was used to determine the weights of the input values due to the quantitative effects of these values, whereas the analytical hierarchic process (AHP) was used for the out values due to qualitative effects. A new hybrid method was formed, embedding the ANN and AHP results into EATWOS. The new hybrid model was then applied to a sample geothermal district heating system for optimization. In this aim, 148 designs were formed throughout the different inlet parameters and evaluated by exergoeconomic and exergoenvironmental analysis to conduct the outputs. © 2023, The Authors. All rights reserved.

关键词： Neural networks

A Comparative Study on Feature Selection for a Risk Prediction Model for Colorectal Cancer

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arXiv 2024年

作者： Cueto-López, Nahúm García-Ordás, Maria Teresa Dávila-Batista, Veronica Moreno, Víctor Aragonés, Nuria Alaiz-Rodríguez, Rocío Department of Electrical Systems and Automatic Engineering Universidad of León Campus de Vegazana s/n León24071 Spain Spain Unit of Biomarkers and Susceptibility Cancer Prevention and Control Programme Catalan Institute ofOncology-IDIBELL L’Hospitalet de Llobregat Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Madrid Spain Madrid Spain

Background and objective: Risk prediction models aim at identifyingpeople at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the leading cancer risk (and protective) factors. Assessing the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the features with more prediction power. Methods: This work is focused on colorectal cancer, assessing several feature ranking algorithms in terms of performance for a set of risk prediction models (Neural Networks, Support Vector Machines (SVM), Logistic Regression, k-Nearest Neighbors and Boosted Trees). Additionally, their robustness is evaluated following a conventional approach with scalar stability metrics and a visual approach proposed in this work to study both similarity among feature ranking techniques as well as their individual stability. A comparative analysis is carried out between the most relevant features found out in this study and features provided by the experts according to the state-of-the-art knowledge. Results: The two best performance results in terms of Area Under the ROC Curve (AUC) are achieved with a SVM classifier using the top-41 features selected by the SVM wrapper approach (AUC=0.693) and Logistic Regression with the top-40 features selected by the Pearson (AUC=0.689). Experiments showed that performing feature selection contributes to classification performance with a 3.9% and 1.9% improvement in AUC for the SVM and Logistic Regression classifier, respectively, with respect to the results using the full feature set. The visual approach proposed in this work allows to see that the Neural Network-based wrapper ranking is the most unstable while the Random Forest is the most stable. Conclusions: This study demonstrates that stability and model performance should be studied jointly as Random Forest turned out to be the most stable

关键词： Stability

controllability Analysis of Multi-Modal Acoustic Particle Manipulation in One-Dimensional Standing Waves

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arXiv 2025年

作者： Wu, Dongjun Perticarari, Guilherme Baasch, Thierry Department of Automatic Control Lund University Box 118 LundSE-221 00 Sweden Master’s in Machine Learning Systems and Control programme Lund University Sweden Department of Biomedical Engineering Lund University Box 118 LundSE-221 00 Sweden

Acoustic manipulation in microfluidic devices enables contactless handling of biological cells for Lab-on-Chip applications. This paper analyzes the controllability of multi-particle systems in a one-dimensional acoustic standing wave system using multi-modal actuation. By modeling the system as a nonlinear control system, we analyze its global and local controllability, quantifying these properties in terms of mode numbers. Our results show that sufficient modes enable dense reachability sets, while mode mixing with 10 modes grants a strict notion of controllability to 80% of the state space in a two-particle system. These findings offer theoretical insights for designing acoustic manipulation algorithms, supporting efficient control in biomedical applications. © 2025, CC BY.

关键词： controllability

Reconfigurable formations of quadrotors on Lissajous curves for surveillance applications

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arXiv 2018年

作者： Borkar, Aseem V. Hangal, Swaroop Arya, Hemendra Sinha, Arpita Vachhani, Leena Interdisciplinary Programme in Systems and Control Engineering Indian Institute of Technology Bombay Mumbai400076 India Department of Aerospace Engineering Indian Institute of Technology Bombay Mumbai400076 India

This paper proposes trajectory planning strategies for online reconfiguration of a multi-agent formation on a Lissajous curve. In our earlier work [2], a multi-agent formation with constant parametric speed was proposed in order to address multiple objectives such as repeated collision-free surveillance and guaranteed sensor coverage of the area with ability for rogue target detection and trapping. This work addresses the issue of formation reconfiguration within this context. In particular, smooth parametric trajectories are designed for the purpose using calculus of variations. These trajectories have been employed in conjunction with a simple local cooperation scheme so as to achieve collision-free reconfiguration between different Lissajous curves. A detailed theoretical analysis of the proposed scheme is provided. These surveillance and reconfiguration strategies have also been validated through simulations in MATLAB® for agents performing parametric motion along the curves, and by Software-In-The-Loop simulation for quadrotors. In addition, they are validated experimentally with a team of quadrotors flying in a motion capture environment. Copyright © 2018, The Authors. All rights reserved.

关键词： Monitoring

Corrigendum to “A new geometric proof of super-twisting control with actuator saturation” [Automatica 87 (2018) 437-441]

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Automatica 2018年 96卷 398-398页

作者： Abhisek K. Behera Asif Chalanga Bijnan Bandyopadhyay ASRI Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Republic of Korea Department of Electronic and Electrical Engineering University College London London UK Interdisciplinary Programme in Systems and Control Engineering Indian Institute of Technology Bombay Mumbai 400 076 India

A multiple model gap-metric based approach to nonlinear quantitative feedback theory

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IFAC-PapersOnLine 2016年第1期49卷 160-165页

作者： R. Jeyasenthil P.S.V. Nataraj Interdisciplinary Programme in Systems and Control Engineering IIT Bombay Mumbai-400 076 India

We propose a simple quantitative feedback theory (QFT) technique for designing robust nonlinear feedback systems. In the proposed approach, multiple linear models are obtained by linearization of the nonlinear plant model at each operating condition, and local QFT controller/filter obtained using standard QFT techniques. Then, global controller/filter effective over the entire operating range are constructed from the local ones by soft switching based on the gap-metric concept. The proposed approach is much simpler and easier than the existing NLQFT approaches where the so-called linear time invariant equivalent (LTIE) plants need to be constructed from the original nonlinear plant set. The proposed method is illustrated via a challenging nonlinear continuous stirred tank reactor (CSTR) example.

关键词： Multiple models Nonlinear control Quantitative feedback theory Robust control

Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping