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4th International Conference on Advanced engineering Theory and Applications, AETA 2017

作者： Sampei, Mitsuji Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9783319698137

Most of controllers for nonlinear systems are designed by using linearly approximated models and by applying linear control theory. In most of such cases, nonlinear control theory cannot improve control performance as long as we are controlling the systems in the vicinity of the equilibrium point. However, there are many under-actuated systems which are not stabilized with this linearization strategy: some sorts of singularity at the equilibrium point cause uncontrollability of the approximated linear model even though the system is controllable in nonlinear control theory. This note will present such nature of under-actuated systems and their control strategies. © Springer International Publishing AG 2018.

关键词： Linear control systems

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Adjacency Constraint for Efficient Hierarchical Reinforcement Learning

arXiv

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arXiv 2021年

作者： Zhang, Tianren Guo, Shangqi Tan, Tian Hu, Xiaolin Chen, Feng The Department of Automation Tsinghua University Beijing100086 China The Beijing Innovation Center for Future Chip Beijing100086 China The LSBDPA Beijing Key Laboratory Beijing100084 China The Department of Civil and Environmental Engineering Stanford University Stanford CA94305 United States The Department of Computer Science and Technology Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing100084 China

Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is large. Searching in a large goal space poses difficulty for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a k-step adjacent region of the current state using an adjacency constraint. We theoretically prove that in a deterministic Markov Decision Process (MDP), the proposed adjacency constraint preserves the optimal hierarchical policy, while in a stochastic MDP the adjacency constraint induces a bounded state-value suboptimality determined by the MDP’s transition structure. We further show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks including challenging simulated robot locomotion and manipulation tasks show that incorporating the adjacency constraint significantly boosts the performance of state-of-the-art goal-conditioned HRL approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Giant and tunable excitonic optical anisotropy in single-crystal CsPbX3 halide perovskites

arXiv

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arXiv 2022年

作者： Ermolaev, G.A. Pushkarev, A.P. Zhizhchenko, A.Yu. Kuchmizhak, A.A. Iorsh, I.V. Kruglov, I. Mazitov, A. Ishteev, A. Konstantinova, K. Saranin, D. Slavich, A.S. Stosic, D. Zhukova, E. Tselikov, G. Di Carlo, Aldo Arsenin, Aleksey V. Novoselov, K.S. Makarov, S.V. Volkov, V.S. Xpanceo Dubai Investment Park First Dubai United Arab Emirates ITMO University School of Physics and Engineering St. Petersburg197101 Russia Far Eastern Federal University Vladivostok690091 Russia Institute of Automation and Control Processes Far Eastern Branch Russian Academy of Science Vladivostok690041 Russia Center for Photonics and 2D Materials Moscow Institute of Physics and Technology Dolgoprudny141700 Russia Moscow127055 Russia LASE – Laboratory of Advanced Solar Energy NUST MISiS Moscow119049 Russia N.N. Semenov Federal Research Center for Chemical Physics Russian Academy of Sciences Moscow119991 Russia CHOSE – Centre of Hybrid and Organic Solar Energy Department of Electronics Engineering Rome Italy University of Manchester ManchesterM13 9PL United Kingdom Institute for Functional Intelligent Materials National University of Singapore Singapore Chongqing 2D Materials Institute Chongqing400714 China

During the last years, giant optical anisotropy demonstrated its paramount importance for light manipulation which resulted in numerous applications ranging from subdiffraction light guiding to switchable nanolasers. In spite of recent advances in the field, achieving continuous tunability of optical anisotropy remains an outstanding challenge. Here, we present a solution to the problem through chemical alteration of the ratio of halogen atoms (X = Br or Cl) in single-crystal CsPbX3 halide perovskites. It turns out that the anisotropy originates from an excitonic resonance in the perovskite, which spectral position and strength are determined by the halogens composition. As a result, we manage to continually modify the optical anisotropy by 0.14. We also discover that the halide perovskite can demonstrate optical anisotropy up to 0.6 in the visible range – the largest value among non-van der Waals materials. Moreover, our results reveal that this anisotropy could be in-plane and out-of-plane, depending on perovskite shape – rectangular and square. Hence, it can serve as an additional degree of freedom for anisotropy manipulation. As a practical demonstration, we created perovskite anisotropic nanowaveguides and show a significant impact of anisotropy on high-order guiding modes. These findings pave the way for halide perovskites as a next-generation platform for tunable anisotropic photonics. Copyright © 2022, The Authors. All rights reserved.

关键词： Perovskite

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Deep learning enabled smart charging technology for electric vehicles

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AIP Conference Proceedings 2022年第1期2527卷

作者： T. Blesslin Sheeba C. Sharanya C. Nayanatara S. K. Indumathi K. Kalins G. Ignisha Rajathi 1Department of ECE R.M.K. Engineering College Chennai India 2Department of ECE Vels Institute of Science Technology and Advanced Studies Chennai India 3Department of EEE Sri Sairam Engineering College Chennai India 4Centre for Automation and Robotics Hindustan Institute of Technology and Science Chennai India 5Department of Science and Humanities Sri Krishna College of Engineering and Technology Coimbatore India 6Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India

Reliability, efficiency, and cost-effectiveness of smart grids are enhanced with power demand softening by means of efficient load management in electric vehicles. In such initiatives, the involvement of EV users may reduce due to the lack of adaptable user-centric approaches. During the connection sessions, the EV charging time is determined using a deep learning algorithm-based smart charging strategy proposed in this paper. Here, the total energy cost of the vehicle is minimized by making charging decisions considering demand time series, pricing, environment, driving, and other auxiliary data. The memorization technique is used for the estimation of the optimal solution of the existing connection sessions in the initial stage. The deep learning models are trained with this existing data and optimal decisions to make suitable decisions in real-time scenarios where car usage or future energy price values are undetermined. A significant reduction in the charging cost is observed by training the neural network with the proposed model. The results obtained are compared to the optimal charging costs computed and are found to be closely similar.

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Recursive Identification of Fractional Time-Delay systems Using Discrete-Time Model

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IFAC-PapersOnLine 2018年第15期51卷 939-944页

作者： Higo, Toshiaki Kawaguchi, Takahiro Itamoto, Masaki Adachi, Shuichi Department of Applied Physics and Physico-Informatics Keio University Yokohama Japan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

In this paper, a recursive identification method for fractional time-delay systems using a discrete-time model is proposed. The fractional time delay is a non-integer multiple of the sampling period for the discrete-time system. We describe the discretization of the fractional time-delay systems and propose a new formulation for system identification. Single-input, single-output systems are transformed to two-input, single-output systems: one transfer function represents system dynamics and the other represents the influence of the time delay. Then, we propose a new recursive identification method for fractional time-delay systems. Finally we verify the effectiveness of the proposed method through numerical examples. © 2018

关键词： Time delay Delay control systems Digital control systems Discrete time control systems Numerical methods Timing circuits Discrete time systems Discrete time model Discretizations Recursive identification Single input , single outputs Single output systems System Dynamics Time delay systems

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Long-term Stability Analysis of Vertical Channel Damping and Kalman Filtering for Inertial Navigation System/Vertical Gauge

Long-term Stability Analysis of Vertical Channel Damping and...

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International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Chan Gook Park Jaehyuck Cha Hojin Ju Department of Mechanical and Aerospace Engineering Automation and Systems Research Institute Seoul National University Seoul Korea Automation and Systems Research Institute Seoul National University Seoul Korea

This paper deals with inertial navigation system (INS) aided by a vertical gauge (VG) for unmanned vehicles in two different schemes, vertical channel damping (VCD) and Kalman filter (KF). As the altitude error of standalone INS goes to diverge quickly, a VG can play a key role in an integrated navigation. A conventional technique to integrate INS with a VG is the VCD scheme which only corrects the vertical channel states by separating them from those of the horizontal channels. On the other hand, in this paper, it is shown that a KF scheme without separation of them can also reduce the horizontal position estimation errors as well by only using a VG. The navigation results of the two different schemes through numerical simulations are compared. Providing the simulation results on the both of correct and incorrect KF measurement error model, it shows the performance of KF scheme considerably depends on the model accuracy.

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Approximation methods for FO-IMC controllers for time delay systems 2

Approximation methods for FO-IMC controllers for time delay ...

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2nd International Conference on Electrical engineering and Green Energy, CEEGE 2019

作者： Muresan, Cristina I. Birs, Isabela R. Prodan, Ovidiu Nascu, Ioan De Keyser, Robin Department of Automation Technical University of Cluj-Napoca Cluj-Napoca Romania DySC Research Group on Dynamical Systems and Control Ghent University Ghent Belgium EDT Group Member of Flanders Make Consortium Belgium Department of Civil Engineering Technical University of Cluj-Napoca Cluj-Napoca Romania

Fractional Order Internal Model control (FO-IMC) is among the newest trends in extending fractional calculus to the integer order control. Approximation of the FO-IMC is one of the key problems. Apart from this, when dealing with time delay systems, the time delay needs also to be approximated. All these approximations can alter the closed loop performance of the controller. In this paper, FO-IMC controllers will be tested in terms of the approximation accuracy. The case study is a first order system with time delay. Several scenarios will be considered, aiming for a conclusion regarding the choice of the approximation method as a function of the process characteristics, closed loop performance and FO-IMC fractional order. To approximate the time delay, two extensively used techniques will be considered, such as the series and Pade approximations. These will be compared to a novel approximation technique. An analysis of the test cases presented show that the series approximation proves more suitable in a single scenario, whereas the novel approximation method produces better results for the rest of the test cases. © 2019 The Authors, published by EDP Sciences.

关键词： Time delay

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Rational design on Cu-based electrochemical catalyst for efficient green ammonia production from nitrate: A review

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Journal of Environmental Chemical engineering 2025年第3期13卷

作者： Karuppiah Veerathangam Nancy Anna Anasthasiya A Yunliang Liu Jingya Yin Peter Phiri Yaxi Li Jingwen Yu Yuanyuan Cheng Naiyun Liu Lei Xu Haitao Li Institute for Energy Research School of Energy and Power Engineering Jiangsu University Zhenjiang 212013 China Materials Research Laboratory for Energy and Environmental Application Department of Chemistry Bharathidasan Institute of Technology (UCE-BIT Campus) Anna University Tiruchirappalli Tamil Nadu 620024 India Systems Engineering Piteira Group International Iberian Nanotechnology Laboratory Avenida Mestre José Veiga s/n Braga 4715-330 Portugal Qingdao Municipal Center for Disease Control and Prevention Qingdao 266033 China Jiangsu Engineering Technology Research Center of Biomass Composites and Addictive Manufacturing Institute of Agricultural Facilities and Equipment Jiangsu Academy of Agricultural Sciences Nanjing Jiangsu 210014 China Key Laboratory for Protected Agricultural Engineering in the Middle and Lower Reaches of Yangtze River Ministry of Agriculture and Rural Affairs Nanjing Jiangsu 210014 China

In this review, the green electrochemical conversion of nitrate (NO 3 - ) into ammonia (NH 3 ) is discussed, this approach will offer an efficient pathway to make high-value-added product production along with the disposal of nitrate pollutants from the wastewater. In recent years, electrochemical reduction methods have attracted much interest in nitrate reduction because of their exceptional selectivity, scalability, and environmental friendliness. This review aims to clarify the role of copper-based catalysts in the electrochemical conversion of NO 3 - to NH 3 and to pinpoint the variables influencing these catalysts efficiency. Cu-based materials have significant benefits in facilitating the selective electroreduction of nitrate to ammonia, as evidenced by their high nitrate conversion efficiency, ammonia selectivity, and ammonia Faradaic efficiency. To understand the need for the NO 3 - RR process, the various nitrate treatment methods and the commercially existing Haber-Bosch process for ammonia synthesis were described. The issue of the NO 3 - RR process, the electrocatalyst need, the mechanism involved in the conversion process, and the role of electrolyte pH were presented. This extensive review will provide an in-depth understanding of the function of Cu-based materials in electrochemical NO 3 - reduction and the development of efficient electrode materials for the addressed reactions. Meanwhile, there has been much discussion of the reaction mechanism, especially in the computational investigation section of the review, which can offer a better understanding of the underlying beneficial features of Cu-based catalysts. Finally, in the conclusion and future perspective part, the vital research area is mentioned.

关键词： Cu-based Catalyst Electrochemical Faradaic Efficiency Green Ammonia Nitrate Reduction

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BNAS: An efficient neural architecture search approach using broad scalable architecture

arXiv

引用

arXiv 2020年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Sun, Zhiquan Philip Chen, C.L. State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electric Engineering University of Science and Technology Beijing Beijing100083 China School of Computer Science & Engineering South China University of Technology Guangzhou Guangdong510006 China College of Navigation Dalian Maritime University Dalian116026 China

Efficient Neural Architecture Search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning. In the phase of architecture search, ENAS employs deep scalable architecture as search space whose training process consumes most of search cost. Moreover, time consuming of model training is proportional to the depth of deep scalable architecture. Through experiments using ENAS on CIFAR-10, we find that layer reduction of scalable architecture is an effective way to accelerate the search process of ENAS but suffers from prohibitive performance drop in the phase of architecture estimation. In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue. On one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we also adopt reinforcement learning and parameter sharing used in ENAS as the optimization strategy of BNAS. Hence, the proposed approach can achieve higher search efficiency. On the other hand, the broad scalable architecture extracts multi-scale features and enhancement representations, and feeds them into global average pooling layer to yield more reasonable and comprehensive representations. Therefore, the performance of broad scalable architecture can be promised. In particular, we also develop two variants for BNAS who modify the topology of BCNN. In order to verify the effectiveness of BNAS, several experiments are performed and experimental results show that 1) BNAS delivers 0.19 days which is 2.37x less expensive than ENAS who ranks the best in reinforcement learning-based NAS approaches, 2) compared with small-size (0.5 millions parameters) and medium-size (1.1 millions parameters) models, the architecture learned by BNAS obtains state-of-the-art performance (3.58% and 3.24% test error) on CIFAR-1

关键词： Topology

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Optimal voltage regulator of power supply system for submersible processing equipment

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IOP Conference Series: Materials Science and engineering 2021年第1期1019卷

作者： V G Bukreev V M Rulevskiy E B Shandarova E A Bystrov Division for Power and Electrical Engineering National Research Tomsk Polytechnic University Tomsk Russia Research Institute of Automation and Electromechanics Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

The article considers the synthesis of the optimal voltage regulator for the electric power supply system of the electromechanical complex of the submersible apparatus with the power transmission through the cable line. Based on the reduced linearized model of the multidimensional nonlinear control object the regulator with adjustable parameters is synthesized, providing the DC voltage stabilization at the power supply system load. The variant of building proportional feedback on output state variables is used for the regulator in the system with parametric and external disturbances. Due to the technical impossibility of measuring the voltage on the load, a combined optimal regulator with negative feedback is organized, which makes it possible to stabilize the voltage at the payload within the specified limits when receiving data from the output filter of voltage source inverter.

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