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American control Conference

作者： Pedro Mercader Kristian Soltesz Alfonso Banos Department of Computer Sciences and Systems Engineering University of Murcia Spain Department of Automatic Control Lund University Sweden

ISBN: (纸本)9781467386838

In many system identification methods, process model parameters are considered stochastic variables. Several methods do not only yield expectations of these, but in addition their variance, and sometimes higher moments. This paper proposes a method for robust synthesis of the proportional-integral-derivative (PID) controller, taking parametric process model uncertainty explicitly into account. The proposed method constitutes a stochastic extension to the well-studied minimization of integrated absolute error (IAE) under H_∞-constraints on relevant transfer functions. The conventional way to find an approximate solution to the extended problem is through Monte Carlo (MC) methods, resulting in high computational cost. In this work, the problem is instead approximated by a deterministic one, through the unscented transform (UT), and its conjugate extension (CUT). The deterministic approximations can be solved efficiently, as demonstrated through several realistic synthesis examples.

关键词： Stochastic processes Robustness Process control Transforms Minimization Transfer functions Optimization

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Butterworth Pattern-based Simultaneous Damping and Tracking controller Designs for Nanopositioning systems

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Frontiers in Mechanical engineering 2016年 2卷

作者： Russell, Douglas San-Millan, Andres Feliu, Vicente Aphale, Sumeet S. School of Engineering University of Aberdeen Aberdeen United Kingdom Department of Electrical Electronic and Automatic Control Engineering University of Castilla-La Mancha Ciadud Real Spain

The Butterworth filter is known to have maximally flat response. Incidentally, the same response is desired in precise positioning systems. This paper presents a method for obtaining a closed-loop Butterworth filter pattern using common control schemes for positioning applications, i.e., Integral Resonant control (IRC), Integral Force Feedback (IFF), Positive Position Feedback (PPF), and Positive Velocity and Position Feedback (PVPF). Simulations show a significant increase in bandwidth over traditional design methods and verify the desired pole placement is achieved. The simulations also show a significant limitation of the achievable bandwidth in the case of IRC, IFF, and PPF. For this reason, only PVPF is considered in experimental analysis. Experiments are performed using a two-axis serial kinematic nanopositioning stage. The results show a significant improvement in bandwidth, from 123 to 370 Hz, and increased positioning accuracy, specifically at the turn-around point, in comparison to the sequentially designed control scheme. © Copyright © 2016 Russell, San-Millan, Feliu and Aphale.

关键词： Butterworth filters

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The use of fractal dimension for purposes of AC motors functional steadiness analysis

The use of fractal dimension for purposes of AC motors funct...

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International Conference on Applied and Theoretical Electricity (ICATE)

作者： Ion Voncilă Mădălin Costin Mihai-Lucian Voncilă Automatic Control and Electrical Engineering Department University. „Dunărea de Jos” of Galati Faculty of Automatic Control and Computers University. Politehnica Bucharest

The paper deals with two analysis methods which can provide enough information regarding functional steadiness of electric drives using AC motors: frequential analysis method (determine the content of harmonics of the electric specific curves), and fractal analysis method (determine the fractal dimension of specific curves of time variation). The paper shows the complementarity of the two methods and the validity of the new method suggested (fractal analysis/fractal dimension) through the non-contradictory nature concerning the influence of any disturbance on the driving system functional steadiness.

关键词： Synchronous motors Drives Fractals Permanent magnet motors Harmonic analysis Frequency conversion Couplings

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Efficient Implementation of Randomized MPC for Miniature Race Cars

Efficient Implementation of Randomized MPC for Miniature Rac...

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European control Conference

作者： Jesús Velasco Carrau Alexander Liniger Xiaojing Zhang John Lygeros Department of Automatic Systems' Engineering Universitat Politècnica de València Spain Automatic Control Laboratory ETH Zurich Switzerland

ISBN: (纸本)9781509025923

This paper describes a data-driven Randomized Model Predictive control (MPC) approach toward autonomous racing of miniature cars. The main challenge in autonomous racing is to drive as fast as possible, without actually exiting the track. Designing such controllers is a challenging task, since (i) the models of the cars are not entirely accurate, and (//) the time to compute the control inputs is limited to tens of milliseconds. The current practice is to resort to standard MPC, where the safety-related constraints are tightened manually. In this paper, we propose an alternative method based on Stochastic MPC, which naturally introduces robustness by design. We approximate the Stochastic MPC problem by means of randomization, implemented as a sparse Randomized MPC problem. We show that the resulting quadratic program can be solved in the range of milliseconds. Moreover, the sparse Randomized MPC formulation can be interpreted as a standard MPC problem whose constraints are, based on collected data, automatically tightened. We show experimentally that the proposed controller can outperform currently implemented controllers. Our results suggest that Randomized MPC is an attractive alternative to standard controllers, due to its ability to introduce robustness without sacrificing performance.

关键词： MPC Efficient Implementation Randomized

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Predictive control for an asymmetric multilevel converter for PV energy injection

Predictive control for an asymmetric multilevel converter fo...

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International Conference on Environment and electrical engineering (EEEIC)

作者： Javier Muñoz Fausto Cadena Patricio Gaisse Marco Rivera Eduardo Espinosa Raúl Gregor Department of Electrical Engineering Universidad de Talca Talca Chile Dept. of Environment and Energy Universidad Católica de la Santisima Concepción Concepcion Chile Lab. of Power and Control Systems Universidad Nacional de Asunción Luque CP Paraguay

ISBN: (纸本)9781538639184

Recent publications on multilevel converters show different topologies and control to obtain an improvement in the sinusoidal current waveform, controlling de DC voltage and the active/reactive power. This paper presents a 27-level asymmetric multilevel converter for photovoltaic injection using a predictive control algorithm that commands the three-phase converter to control the active power injected to the grid. The analyses include the detailed description of the converter valid states as well as the discrete time model of the equivalent circuit. The control strategy considers a master linear controller in charge to track the Maximum Power Point and a slave loop that includes a predictive controller, which is responsible of the current tracking. The proposal is validated through simulated results that illustrates the dynamic behavior of the converter under irradiance changes.

关键词： Mathematical model Prediction algorithms Integrated circuit modeling Topology Voltage control Predictive models Cost function

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Robust and Scalable Power System State Estimation via Composite Optimization

arXiv

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arXiv 2017年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center and the Electrical and Computer Engineering Department University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200072 China

—In today’s cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares (WLS) one. However, due to nonconvexity and nonsmoothness, existing LAV solvers based on linear programming are typically slow, hence inadequate for real-time system monitoring. This paper develops two novel algorithms for efficient LAV estimation, which draw from recent advances in composite optimization. The first is a deterministic linear proximal scheme that handles a sequence of (5 ∼ 10 in general) convex quadratic problems, each efficiently solvable either via off-the-shelf toolboxes or through the alternating direction method of multipliers. Leveraging the sparse connectivity inherent to power networks, the second scheme is stochastic, and updates only a few entries of the complex voltage state vector per iteration. In particular, when voltage magnitude and (re)active power flow measurements are used only, this number reduces to one or two, regardless of the number of buses in the network. This computational complexity evidently scales well to large-size power systems. Furthermore, by carefully mini-batching the voltage and power flow measurements, accelerated implementation of the stochastic iterations becomes possible. The developed algorithms are numerically evaluated using a variety of benchmark power networks. Simulated tests corroborate that improved robustness can be attained at comparable or markedly reduced computation times for medium- or large-size networks relative to existing alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Gas Phase Train in Upstream Oil & Gas Fields: PART-I Model Development

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IFAC-PapersOnLine 2016年第7期49卷 875-881页

作者： Al-Naumani, Y.H. Rossiter, J.A. Bahlawi, S.J. Quality Measuring Instruments and Metering Supervisor Petroleum Development Oman LLC P.O. Box 81 Postal code 100 Muscat Oman Department of Automatic Control and Systems Engineering University of Sheffield S1 3JD Sheffield United Kingdom Production Support Team Lead Harweel United Kingdom

The prime contribution of this paper is to provide a large scale system (LSS) model for the gas phase operation in upstream oil and gas plants. The process model consists of the three main gas conditioning processes which exist in most upstream oil and gas processing plants; these are gas sweetening, gas dehydration, and hydrocarbon dew-pointing. The function of such a model is to provide a realistic process representation to test and verify different process control approaches, specifically those which deal with highly interactive control loops. © 2016

关键词： Process control Gallium Gas industry Gas plants Gases Hydrocarbon refining Large scale systems Natural gas fields Oil field development control approach Gas dehydrations Hydrocarbon dew pointing Interactive control Model development Process Modeling Process representation Upstream Oil

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A comparison of fuzzy identification methods on benchmark datasets

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IFAC-PapersOnLine 2016年第5期49卷 31-36页

作者： Aleksovski, Darko Dovan, Dejan Deroski, Sao Kocijan, Ju Department of Knowledge Technologies Joef Stefan Institute Jamova cesta 39 Ljubljana Slovenia Faculty of Electrical Engineering University of Ljubljana Traka cesta 25 Ljubljana Slovenia Department of Systems and Control Joef Stefan Institute University of Nova Gorica Nova Gorica Jamova cesta 39 Vipavska 13 Ljubljana Slovenia

In this paper, we address the task of discrete-time modeling of nonlinear dynamic systems. We use Takagi-Sugeno fuzzy models built by LOLIMOT and SUHICLUST, as well as ensembles of LOLIMOT fuzzy models to accurately model nonlinear dynamic systems from input-output data. We evaluate these approaches on benchmark datasets for three laboratory processes. The measured data for the case studies are publicly available and are used for development, testing and benchmarking of system identification algorithms for nonlinear dynamic systems. Our experimental results show that SUHICLUST produces smaller models than LOLIMOT for two of the three datasets. In terms of error, ensembles of LOLIMOT models improve the predictive performance over that of a single LOLIMOT or SUHICLUST model. © 2016

关键词： Dynamical systems Fuzzy clustering Identification (control systems) Nonlinear dynamical systems Discrete time model ensembles Fuzzy identification method Fuzzy model identification Predictive performance System identification algorithms Takagi Sugeno fuzzy models Tree partitioning

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Model of integration of embedded systems via CoAP protocol of Internet of Things

Model of integration of embedded systems via CoAP protocol o...

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International Conference on Applied Electronics, AE

作者： Peter Peniak Mária Franekova Department of Control and Information Systems Faculty of Electrical Engineering Žilina Slovak Republic

ISBN: (纸本)9781509017270

This article deals with Internet of Things as an integration platform for embedded systems. The problem of traditional hierarchical communication is challenged by direct peer-to-peer communication model among various embedded devices. CoAP protocol is selected as a potential candidate to meet that requirements and enable overall integration among embedded systems and Cloud systems. In addition, there is evaluated an option to use dedicated IoT integration gateway to enable smooth communication of devices with various maturity level protocols. IoT gateway can support protocol translation and various proxy functions for connection with Cloud systems.

关键词： Protocols Internet of things Embedded systems Logic gates Cloud computing Peer-to-peer computing

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Removal of Power Line Interference in EEG Signals with Spike Noise Based on Robust Adaptive Filter

Removal of Power Line Interference in EEG Signals with Spike...

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IEEE Region 10 Conference

作者： Jianqiang Lin Xu Sun Jiafei Wu Shing-Chow Chan Weichao Xu Department of Electrical and Electronic Engineering The University of Hong Kong Department of Automatic Control School of Automation Guangdong University of Technology

ISBN: (纸本)9781509025985

This paper proposes a general robust active noise control (RANC) framework for removing power line interference (PLI) from the Electroencephalogram (EEG) signals when both reference and primary signals are contaminated by spike noise. It is obtained by exploiting the robust property of M-estimation function against impulses. According to this new framework, two commonly used methods, namely, least mean squares (LMS) and normalized least mean squares (NLMS), are extended in parallel to least mean M-estimate (LMM) and normalized least mean M-estimate (NLMM), respectively. Experimental results based on the benchmark MIT-BIH Polysomnographic Database show the sufficient capability of our methods for cancelling PLI in EEG signals with noisy observations and excellent performance for rejecting spikes existing in PLI and EEG signals.

关键词： Power Line Interference Electroencephalogram Spike Noise Robust Adaptive Filter

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