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12th IFAC Symposium on Large Scale Systems: Theory and Applications, LSS 2010

作者： Brdys, Mietek A. Department of Control Systems Engineering Gdańsk University of Technology ul. G. Narutowicza 11/12 80-233 Gdańsk Poland School of Electronic Electrical and Computer Engineering College of Engineering and Physical Sciences University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom

ISBN: (纸本)9783902661913

A control system engineering approach to monitoring and control of critical infrastructure systems (CIS) is presented in the paper. A multilayer structure for an intelligent reconfigurable agent operating within a single region of a CIS is derived first. Methods and algorithms for synthesising the layers are proposed so that the agent can autonomously curry out required control activities under wide range of operating conditions. Next an approach to develop the multiagent structures, which are suitable for monitoring and control of an overall CIS are discussed. It is based on the hierarchical and decentralised structuring the agent layers. The proposals are applied to integrated wastewater treatment case - study system. Copyright © 2010 IFAC.

关键词： Model predictive control

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Inferring latent domains for unsupervised deep domain adaptation

arXiv

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arXiv 2021年

作者： Mancini, Massimiliano Porzi, Lorenzo Buló, Samuel Rota Caputo, Barbara Ricci, Elisa Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Mapillary Research Graz Austria DAUIN Department of Control and Computer Engineering Politecnico di Torino Turin Italy Italian Institute of Technology Turin Italy Fondazione Bruno Kessler Trento Italy Department of Information Engineering and Computer Sciene University of Trento Trento Italy

Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available by leveraging information from annotated data in a source domain. Most deep UDA approaches operate in a single-source, single-target scenario, i.e. they assume that the source and the target samples arise from a single distribution. However, in practice most datasets can be regarded as mixtures of multiple domains. In these cases, exploiting traditional single-source, single-target methods for learning classification models may lead to poor results. Furthermore, it is often difficult to provide the domain labels for all data points, i.e. latent domains should be automatically discovered. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets and exploiting this information to learn robust target classifiers. Specifically, our architecture is based on two main components, i.e. a side branch that automatically computes the assignment of each sample to its latent domain and novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Optimized management and control of household energy demand supplied from solar and traditional electrical and gas energy sources

Optimized management and control of household energy demand ...

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12th IFAC Symposium on Large Scale Systems: Theory and Applications, LSS 2010

作者： Pawlowski, Michal Brdyś, Mietek A. Department of Control Systems Engineering Gdańsk University of Technology ul. G. Narutowicza 11/12 80-233 Gdańsk Poland School of Electronic Electrical and Computer Engineering College of Engineering and Physical Sciences University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom

ISBN: (纸本)9783902661913

The paper considers a complex problem of the optimized management and control of energy supplied from solar and traditional electrical and gas energy sources into a single-family house in order to meet required temperature in the house. Solving the problem is approached by applying the control engineering methods. The system model dynamics is derived and simulated in order to learn its essentials needed for designing the management and control architecture. The multilayer architecture including actuators of the control signals is derived and discussed. A model predictive control is proposed as a base for designing the control algorithms.

关键词： Solar energy

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Enabling Smart Agriculture in Nigeria: Application of Digital-Twin Technology 1

Enabling Smart Agriculture in Nigeria: Application of Digita...

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1st International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2021

作者： Elijah, Olakunle Rahim, Sharul Kamal A. Emmanuel, Abioye Abiodun Salihu, Yahaya Otuoze Usman, Zubair Gatugel Jimoh, Adinoyi Momoh Wireless Communication Center School of Electrical Engineering University of Technology Malaysia Johor Bahru Malaysia School of Electrical Engineering University of Technology Malaysia Control and Mechatronics Engr. Dept. Johor Bahru Malaysia Kaduna Polytechnic Computer Engineering Department Kaduna Nigeria Saudi Telecommunication Company Riyadh Saudi Arabia

ISBN: (纸本)9781665434935

The digital twin (DT) is an emerging technology and one of the industry 4.0 technologies. The research in the application of DT in agriculture is currently attracting research interest. In this paper, we provide an overview of DT and discuss possible areas in which it can enable smart agriculture in Nigeria. The benefits and challenges of deploying the DT are also presented. Finally, methods that can be adopted in the application of DT towards solving the problems facing Nigeria's agriculture sector are proposed. © 2021 IEEE.

关键词： Agriculture

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An improved parameterized approach for real time optimal motion planning of AUV moving in dynamic environment

An improved parameterized approach for real time optimal mot...

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2013 6th IEEE Conference on Robotics, Automation and Mechatronics, RAM 2013

作者： Luo, Wenhao Peng, Jun Wang, Jing Department of Automation Shanghai Jiao Tong University Ministry of Education of China Shanghai 200240 China School of Information Science and Engineering Central South University Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Changsha 410083 China Computer Engineering Program Bethune-Cookman University Daytona Beach FL 32114 United States

ISBN: (纸本)9781479911998

In this paper, we present an improved analytic method to the optimal trajectory generation of an autonomous underwater vehicle (AUV) in a dynamic environment. The proposed approach explicitly incorporates both the AUV kinematic and the geometric constraints due to dynamic obstacles and the terrain while rendering the near-shortest path by a performance index related to the path length. In particular, the proposed design is based on a family of parameterized trajectories determined by three adjustable parameters, which provides a unified way to reformulate the geometric constraints and performance index into a set of parameterized constraint equations. To that end, such a constrained optimization problem boils down to optimize those adjustable parameters, which can be analytically solved in the parameter space. The proposed solution enhances the methodologies of real-time path planning for robots in 3D environment. Simulation results verify the effectiveness of the proposed method. © 2013 IEEE.

关键词： Autonomous underwater vehicles

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Application of neuro-fuzzy models in short term electricity load forecast

Application of neuro-fuzzy models in short term electricity ...

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International CSI computer Conference, CSICC

作者： A. R. Koushki M. Nosrati Maralloo C. Lucas A. Kalhor Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

One of the important requirements for operational planning of electrical utilities is the prediction of hourly load up to several days, known as short term load forecasting (STLF). Considering the effect of its accuracy on system security and also economical aspects, there is an on-going attention toward putting new approaches to the task. Recently, neuro fuzzy modeling has played a successful role in various applications over nonlinear time series prediction. This paper presents a neuro-fuzzy model for the application of short-term load forecasting. This model is identified through locally liner model tree (LoLiMoT) learning algorithm. The model is compared to a multilayer perceptron and Kohonen classification and intervention analysis. The models are trained and assessed on load data extracted from EUNITE network competition.

关键词： Predictive models Load forecasting Multilayer perceptrons Economic forecasting Power system modeling Power generation economics Fuel economy Artificial neural networks Power system economics Temperature

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Fixation of competing strategies when interacting agents differ in the time scale of strategy updating

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Physical Review E 2016年第3期94卷 032407-032407页

作者： Jianlei Zhang Franz J. Weissing Ming Cao Department of Automation College of Computer and Control Engineering Nankai University People's Republic of China Theoretical Biology Group Centre for Ecological and Evolutionary Studies University of Groningen The Netherlands Network Analysis and Control Group Institute for Industrial Engineering University of Groningen The Netherlands

A commonly used assumption in evolutionary game theory is that natural selection acts on individuals in the same time scale; e.g., players use the same frequency to update their strategies. Variation in learning rates within populations suggests that evolutionary game theory may not necessarily be restricted to uniform time scales associated with the game interaction and strategy adaption evolution. In this study, we remove this restricting assumption by dividing the population into fast and slow groups according to the players' strategy updating frequencies and investigate how different strategy compositions of one group influence the evolutionary outcome of the other's fixation probabilities of strategies within its own group. Analytical analysis and numerical calculations are performed to study the evolutionary dynamics of strategies in typical classes of two-player games (prisoner's dilemma game, snowdrift game, and stag-hunt game). The introduction of the heterogeneity in strategy-update time scales leads to substantial changes in the evolution dynamics of strategies. We provide an approximation formula for the fixation probability of mutant types in finite populations and study the outcome of strategy evolution under the weak selection. We find that although heterogeneity in time scales makes the collective evolutionary dynamics more complicated, the possible long-run evolutionary outcome can be effectively predicted under technical assumptions when knowing the population composition and payoff parameters.

关键词： Complex systems Evolutionary dynamics Social systems Social networks

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Universal enzymatic numerical P systems with small number of enzymatic variables

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Science China(Information Sciences) 2018年第9期61卷 38-49页

作者： Zhiqiang ZHANG Tingfang WU ANDrei P■UN Linqiang PAN Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Department of Computer Science Faculty of Mathematics and Computer ScienceUniversity of Bucharest Bioinformatics Department National Institute of Research and Development for Biological Sciences School of Electric and Information Engineering Zhengzhou University of Light Industry

Numerical P systems(for short, NP systems) are distributed and parallel computing models inspired from the structure of living cells and economics. Enzymatic numerical P systems(for short, ENP systems) are a variant of NP systems, which have been successfully applied in designing and implementing controllers for mobile robots. Since ENP systems were proved to be Turing universal, there has been much work to simplify the universal systems, where the complexity parameters considered are the number of membranes, the degrees of polynomial production functions or the number of variables used in the *** the number of enzymatic variables, which is essential for ENP systems to reach universality, has not been investigated. Here we consider the problem of searching for the smallest number of enzymatic variables needed for universal ENP systems. We prove that for ENP systems as number acceptors working in the all-parallel or one-parallel mode, one enzymatic variable is sufficient to reach universality; while for the one-parallel ENP systems as number generators, two enzymatic variables are sufficient to reach *** results improve the best known results that the numbers of enzymatic variables are 13 and 52 for the all-parallel and one-parallel systems, respectively.

关键词： bio-inspired computing numerical P system mobile robot membrane controller universality

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The Minimum Principle for Hybrid Systems with Partitioned State Space and Unspecified Discrete State Sequence

The Minimum Principle for Hybrid Systems with Partitioned St...

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2010 49th IEEE Conference on Decision and control

作者： Benjamin Passenberg Peter E. Caines Marion Sobotka Olaf Stursberg Martin Buss Institute of Automatic Control Department of Electrical Engineering and Information Technology Technische Universitat Munchen Munchen Germany Department of Electrical and Computer Engineering and the Center for Intelligent Machines McGill University Montreal Canada Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Kassel Germany

ISBN: (纸本)9781424477456

The hybrid minimum principle (HMP) gives necessary conditions to be satisfied for optimal solutions of a hybrid dynamical system. In particular, the HMP accounts for autonomous switching between discrete states that occurs whenever the trajectory hits switching manifolds. In this paper, the existing HMP is extended for hybrid systems with partitioned state space to provide necessary conditions for optimal trajectories that pass through an intersection of switching manifolds. This extension is especially useful for the numerical solution of hybrid optimal control problems as it allows for algorithms with significant reduction of computational complexity. Algorithms based on previous versions of the HMP solve separate optimal control problems for each possible sequence of discrete states. The extension enables us to consider the optimal sequence as subject of optimal control that is varied and finally determined during a single optimization run. A first numerical result illustrates the effectiveness of an algorithm based on the extended HMP.

关键词： state space minimum principle hybrid systems Manifolds Optimal control DYNAMICAL SYSTEMS discrete element

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Panel discussion whitherto robust control 4

Panel discussion whitherto robust control

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4th IFAC Symposium on Robust control Design, ROCOND 2003

作者： Tempo, Roberto Basar, Tamer Khargonekar, Pramod P. Kokotovic, Petar V. Scherer, Carsten W. IEIIT-CNR Politecnico di Torino Italy Coordinated Science Laboratory University of Illinois Urbana-Champaign United States College of Engineering University of Florida Gainesville United States Department of Electrical and Computer Engineering University of California Santa Barbara United States Mechanical Engineering Systems and Control Group Delft University of Technology Netherlands

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