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An integrated control of shading blinds, natural ventilation, and HVAC systems for energy saving and human comfort

An integrated control of shading blinds, natural ventilation...

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2010 IEEE International Conference on Automation Science and engineering, CASE 2010

作者： Sun, Biao Luh, Peter B. Jia, Qing-Shan Jiang, Ziyan Wang, Fulin Song, Chen Tsinghua University Beijing 100084 China Department of Electrical and Computer Engineering University of Connecticut Storrs CT United States Department of Building Science Tsinghua University Beijing 100084 China United Technology Research Center China Tsinghua-UTC Research Institute for Integrated Building Energy Safety and Control Systems Beijing 100084 China

ISBN: (纸本)9781424454471

Improving the control of shading blinds, lights, natural ventilation, and HVAC systems while satisfying human comfort requirements can result in significant energy cost savings with time-of-day electricity pricing. Traditionally, the above-mentioned devices are controlled separately. In this paper, a novel formulation for the integrated control and the corresponding solution methodology are presented. The problem is to minimize daily energy costs of lights and HVAC systems while satisfying equipment capacities, system dynamics, and human comfort. The problem is complicated since 1) individual rooms are coupled as they compete for the HVAC with limited capacity and nonlinear characteristics, and 2) the problem is believed to be NP-hard in view that decision variables are all discrete. A solution methodology that combines Lagrangian relaxation and stochastic dynamic programming is developed within the surrogate optimization framework to obtain near-optimal strategies. These strategies are further refined to become novel control rules for easy practical implementation. Numerical simulation results show that both of the above strategies can effectively reduce the total energy cost, and that the integrated control works better than selected traditional control strategies. © 2010.

关键词： Integrated control

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On the optimal solutions of the infinite-horizon linear sensor scheduling problem

On the optimal solutions of the infinite-horizon linear sens...

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IEEE Conference on Decision and control

作者： Wei Zhang Michael P. Vitus Jianghai Hu Alessandro Abate Claire J. Tomlin Department of Electrical Engineering and Computer Sciences University of California Berkeley USA Department of Aeronautics and Astronautics University of Stanford CA USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Delft Center of Systems and Control Delft University of Technnology Netherlands

This paper studies the infinite-horizon sensor scheduling problem for linear Gaussian processes with linear measurement functions. Several important properties of the optimal infinite-horizon schedules are derived. In particular, it is proved that under some mild conditions, both the optimal infinite-horizon average-per-stage cost and the corresponding optimal sensor schedules are independent of the covariance matrix of the initial state. It is also proved that the optimal estimation cost can be approximated arbitrarily closely by a periodic schedule with a finite period, and moreover, the trajectory of the error covariance matrix under this periodic schedule converges exponentially to a unique limit cycle. These theoretical results provide valuable insights about the problem and can be used as general guidelines in the design and analysis of various infinite-horizon sensor scheduling algorithms.

关键词： Schedules Covariance matrix Trajectory Optimal scheduling Estimation Scheduling Processor scheduling

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On the Infinite Horizon Constrained Switched LQR Problem

On the Infinite Horizon Constrained Switched LQR Problem

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2010 49th IEEE Conference on Decision and control

作者： Maximilian Balandat Wei Zhang Alessandro Abate Department of Electrical Engineering and Information Technology Technische Universitat Darmstadt Darmstadt Germany Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Delft Center for Systems and Control Technische Universiteit Delft Delft The Netherlands

ISBN: (纸本)9781424477456

This paper studies the Discrete-Time Switched LQR problem over an infinite time horizon subject to polyhedral constraints on state and control input. The overall constrained, infinite-horizon problem is split into two subprob-lems: (i) an unconstrained, infinite-horizon problem and (ii) a constrained, finite-horizon one. We derive a stationary suboptimal policy for problem (i) with analytical bounds on its optimality, and develop a formulation of problem (ii) as a Mixed-Integer Quadratic Program. By introducing the concept of a safe set, the solutions of the two subproblems are combined to achieve the overall control objective. It is shown that, by proper choice of the design parameters, the error of the overall sub-optimal solution can be made arbitrarily small. The approach is tested through a numerical example.

关键词： infinite horizon design parameter discrete-time numerical example optimality

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Selection of optimal EEG channels for classification of signals correlated with alcohol abusers

Selection of optimal EEG channels for classification of sign...

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International Conference on Signal Processing Proceedings (ICSP)

作者： M. Alavash Shooshtari S. Kamaledin Setarehdan Biomedical Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Control and Intelligent Processing Center of Excellence Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran

Many brain events and disorders can be detected by analyzing electroencephalograms (EEGs). Also the availability of quantitative biological markers that are correlated with qualitative psychiatric phenotypes helps us to utilize automated methods to diagnose and classify these phenotypes. One such a psychiatric phenotype is alcoholism. In this study a method to select an optimal subset of EEG channels for the purpose of practical classification of alcohol abusers from normal subjects is proposed, which is based on combination of model-based spectral analysis and correlation matrices. The EEG signals were recorded when the subjects were represented with single trial visual stimuli. The proposed method proved successful in selecting an optimum number of channels which achieved acceptable average classification accuracy.

关键词： Electroencephalography Support vector machines Correlation Visualization Accuracy Feature extraction Brain modeling

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Model Predictive Direct Torque control of permanent magnet synchronous motors

Model Predictive Direct Torque Control of permanent magnet s...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Tobias Geyer Giovanni A. Beccuti Georgios Papafotiou Manfred Morari Department of Electrical and Computer Engineering University of Auckland New Zealand ABB Corporate Research Center Baden-Dattwil Switzerland Automatic Control Laboratory Department of Information Technology and Engineering ETH Zurich Zurich Switzerland

Model Predictive Direct Torque control (MPDTC) is a recent computational control methodology that combines the merits of Model Predictive control (MPC) with the ones of Direct Torque control (DTC). Specifically, with respect to standard DTC, the converter's switching frequency and/or losses are considerably reduced, while at the same time the Total Harmonic Distortion (THD) levels of the phase currents and the torque are improved. Moreover, DTC's favorable dynamic and robustness properties are preserved. This paper presents an MPDTC scheme for a permanent magnet synchronous motor that achieves long prediction horizons in the range of up to 150 time-steps through the use of extrapolation and bounds. A discrete-time internal controller model of the drive system is derived from the physical equations. Simulation results for a three-level voltage source inverter indicate that such an MPDTC scheme, compared to an industry standard controller, is capable of reducing the switching losses and the switching frequency by up to 50%, and the torque THD by 25%, while leaving the current THD unchanged.

关键词： Switches Inverters Stators Switching frequency Mathematical model Predictive models

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Stochastic Differential Dynamic Programming

Stochastic Differential Dynamic Programming

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American control Conference

作者： Evangelos Theodorou Yuval Tassa Emo Todorov Computational Learning and Motor Control Lab Departments of Computer Science and Neuroscience University of Southern California Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel Department of Computer Science and Engineering and the Department of Applied Mathematics University of Washington Seattle WA

ISBN: (纸本)9781424474264

Although there has been a significant amount of work in the area of stochastic optimal control theory towards the development of new algorithms, the problem of how to control a stochastic nonlinear system remains an open research topic. Recent iterative linear quadratic optimal control methods iLQG [1], [2] handle control and state multiplicative noise while they are derived based on first order approximation of dynamics. On the other hand, methods such as Differential Dynamic Programming expand the dynamics up to the second order but so far they can handle nonlinear systems with additive noise. In this work we present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state and control multiplicative process noise, and proceed to derive the second-order expansion of the cost-to-go. We find the correction terms that arise from the stochastic assumption. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP.

关键词： dynamic programming theory of optimal control multiplicative noise Quartic stochastic derivative Additive noise correct entry dynamics Work area Handles

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Optimization of Image Fusion using Genetic Algorithms and Discrete Wavelet Transform

Optimization of Image Fusion using Genetic Algorithms and Di...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Chaunté W. Lacewell Mohamed Gebril Ruben Buaba Abdollah Homaifar Department of Electrical and Computer Engineering Autonomous Control and Information Technology Center NOAA ISET Cooperative Science Center North Carolina Agriculture and Technical State University Greensboro NC USA

Image fusion is the process of combining the most relevant information from multiple source images to obtain an accurate fused image. In this paper, we want to fuse visual and thermal satellite images. In order to provide enhanced information, we have investigated techniques of image fusion to obtain the most accurate information. This paper presents a technique which will produce an accurate fused image using discrete wavelet transform (DWT) for feature extraction and using Genetic Algorithms (GAs) to get the more optimized combined image. The performance of the proposed image fusion scheme is evaluated with mutual information (MI), root mean square error (RMSE), and it is also compared to the fused image that is generated by using Pixel Level GA based Image Fusion (PLGA_IF) and Discrete Wavelet Transform based Image Fusion (DWT_IF) techniques. Simulation results conducted with DWT and GA show that the proposed method outperforms the existing image fusion algorithms.

关键词： Discrete wavelet transforms Image fusion Gallium Genetics Satellites Search problems

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Nonlinear Model Predictive control using sampling and goal-directed optimization

Nonlinear Model Predictive Control using sampling and goal-d...

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IEEE Conference on control Technology and Applications (CCTA)

作者： Damion D. Dunlap Charmane V. Caldwell Emmanuel G. Collins Department of Mechanical Engineering and the Center for Intelligent Systems Control and Robotics (CISCOR) Florida Agricultural and Mechanical University Tallahassee FL USA Department of Electrical and Computer Engineering CISCOR of the FAMU-FSU COE USA Department of Mechanical Engineering CISCOR of the FAMU-FSU COE USA

In this paper a novel method called Sampling-Based Model Predictive control (SBMPC) is proposed as an efficient MPC algorithm to generate control inputs and system trajectories. The algorithm combines the benefits of sampling-based motion planning with MPC while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization method (e.g., A*) in place of standard numerical optimization. This formulation of MPC readily applies to systems with nonlinear dynamics and avoids the local minima which can limit the performance of MPC algorithms implemented using traditional, derivative-based, nonlinear programming. The SBMPC algorithm is compared with a more standard online MPC algorithm using cluttered environment navigation for an Ackerman steered vehicle and a set point problem for a nonlinear, continuous stirred-tank reactor (CSTR).

关键词： Optimization Planning Vehicles Prediction algorithms Trajectory Heuristic algorithms Predictive models

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Parameter identification of a Linear Permanent Magnet motor using Particle Swarm Optimization

Parameter identification of a Linear Permanent Magnet motor ...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Tithiwat Therdbankerd Peerayot Sanposh Nattapon Chayopitak Hideaki Fujita Intelligent Mechatronics Automation Robotics and Control Laboratory Department of Electrical Engineering Kasetsart University Bangkok Thailand Industrial Control and Automation Laboratory National Electronics and Computer Technology Center Klong Luang Pathum Thani Thailand Department of Electrical and Electronic Engineering Tokyo Institute of Technology Meguro Tokyo Japan

ISBN: (纸本)9781424456062;9789746724913

Accurate and effective parameter identification is an important engineering task in high performance control system design. One emerging approach to effectively identify such nonlinear or dynamic unknown parameters is to use Particle Swarm Optimization (PSO) algorithm. Linear Permanent Magnet (LPM) motor is a high performance actuator employed in many applications that require direct linear motion without mechanical transmission for high acceleration and accurate positioning. Therefore, accurate motor parameters are necessary to effectively control the LPM motors. This paper proposes a simple PSO based method with chirp inputs to identify the LPM motor's parameters. The simulations and experiments are conducted to verify the results and determine the effectiveness of the proposed method.

关键词： Parameter estimation Permanent magnet motors Particle swarm optimization Synchronous motors Signal processing Robotics and automation Chirp Intelligent robots control systems Laboratories

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Prediction of financial time series with recurrent LoLiMot (locally linear model tree)

Prediction of financial time series with recurrent LoLiMot (...

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International Conference on computer and Automation engineering, ICCAE

作者： Hossein Chegini Caro Lucas Science and research Branch/Computer Science Islamic Azad university of Tehran Tehran Iran Electrical and Computer Engineering Department Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran

ISBN: (纸本)9781424455690

The tolerance and non-stability in financial indexes make changes to other sub-systems like human resources, economics, factory productions and etc. Having underling knowledge and a model to simulate such systems obtains a fine vision to estimate further and calculate hard-decision making tasks before execution like: dept from banks, cash injecting and insurance services. Using Neuro-fuzzy networks are one of the most powerful tools for this estimation. The particular locally linear model type of these networks called LoLiMot are in interest because of their linear training and construction optimization. These network can be much efficient when be a recurrent network why can better capture the dynamism's order of dynamic processes. The Locally linear Neuro-Fuzzy model (LoLiMot) here is as basis for making recurrent. In this paper this network with a global state feedback is implemented and the accuracy and the results of this recurrent network on Dow Jones index as a financial time series are compared with the static LoLiMot. The obtained results were better.

关键词： Predictive models State feedback Neurons Output feedback Power system modeling Gaussian processes control engineering computing Process control Intelligent control computer science

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