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European control Conference

作者： Dorian Florescu Daniel Coca Department of Automatic Control and Systems Engineering The University of Sheffield

ISBN: (纸本)9781467371605

This paper proposes a model and algorithm to compute the time encoded output of a linear filter directly from the time encoded input. The time encoding machine considered in this study is the integrate-and-fire neuron model. The proposed direct encoding approach is much faster than the indirect approach, involving simulating the filter output and subsequent encoding. A numerical simulation study is used to illustrate the approach.

关键词： Neurons Encoding Heuristic algorithms Prediction algorithms Computational modeling Signal processing algorithms Decoding

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L2-gain analysis of systems with clock offsets

L2-gain analysis of systems with clock offsets

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American control Conference

作者： Masashi Wakaiki Kunihisa Okano Joao P. Hespanha Department of Electrical and Electronic Engineering Chiba University 1-33 Yayoi-cho Inage-ku 263-8522 Japan Center for Control Dynamical-systems and Computation (CCDC) University of California Santa Barbara 93106-9560 USA

ISBN: (纸本)9781467386838

In this paper, we analyze the L2-gain of sampled-data systems with asynchronous sensing and control. The closed-loop system has time-varying clock offsets and sampling intervals and can be modeled as a parameter-uncertain system. We use a lifting approach and a gridding and norm-bounded approach in order to analyze intersample behavior and deal with parameter uncertainty, respectively. An upper bound on the L~2-gain is derived via linear matrix inequalities. We illustrate the proposed method by applying it to an active suspension system.

关键词： Linear matrix inequalities Clocks Closed loop systems Time-varying systems Delays Stability analysis Upper bound Closed loop systems Timing devices linear matrix inequalities time-varying systems Upper bound Stability analysis clock skew System analyses delay offsets Active Suspension systems gridding

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Classification of Radionuclides on Polyvinyl Toluene Radiation Portal Monitors by a Neural Network Based System§

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IFAC-PapersOnLine 2015年第21期48卷 852-857页

作者： Blázquez, L. Felipe Aller, Fernando Vrublevskaya, Svetlana Fombellida, Jorge Valtuille, Eduardo Department of Electrical and Automatic Control and Systems Engineering University of León E.II.I.I. Campus de Vegazana s/n 24071 León Tel.: 34 987293471 Spain Centre of Excellence in Security Systems. Indra León Tel.: 34 987849888 Spain

To detect radiation sources in containers, radiation portal monitors (RPMs) are normally used. Among the various types of RPM, polyvinyl toluene (PVT) monitors are the most widely used, thanks to their lower cost. However, they provide only low resolution when energy spectra are measured. For this reason, it is currently not possible to identify what kind of radiation source is being measured, only that there is some radiation source present, when an alarm is triggered in a PVT monitor. In this case, a second inspection is necessary in order to distinguish between innocent alarms and those due to potentially dangerous radioactive material. This second inspection involves a considerable outlay of money and time at frontiers. The main purpose of this paper is to propose an algorithm which can be applied so as to reduce the number of innocent alarms purely on the basis of the data obtained by the PVT monitors. To this end, an intelligent system based on two stages is proposed: a preprocessing of the energy spectrum and a neural network based pattern recognition module, which classifies the radiation source into one of the standard groups. This intelligent system has been tested with real radiation sources, whose energy spectra were measured with two different PVT detectors, yielding good results. © 2015

关键词： Spectrum analysis

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A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

A motor imagery based brain-computer interface system via sw...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Shang-Lin Wu Yu-Ting Liu Kuang-Pen Chou Yang-Yin Lin Jie Lu Guangquan Zhang Chun-Hsiang Chuang Wen-Chieh Lin Chin-Teng Lin Brain Research Center Institute of Electrical Control Engineering Institute of Computer Science and Engineering Electronic Systems Research Division National Chung-Shan Institute of Science and Technology (NCSIST) Taoyuan Taiwan Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

ISBN: (纸本)9781509006274

A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.

关键词： Electroencephalography Feature extraction Wireless sensor networks Wireless communication Electrodes Particle swarm optimization Microcontrollers

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Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network

Driving fatigue prediction with pre-event electroencephalogr...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Yu-Ting Liu Shang-Lin Wu Kuang-Pen Chou Yang-Yin Lin Jie Lu Guangquan Zhang Wen-Chieh Lin Chin-Teng Lin Brain Research Center Institute of Electrical Control Engineering Institute of Computer Science and Engineering Electronic Systems Research Division National Chung-Shan Institute of Science and Technology (NCSIST) Taoyuan Taiwan FacuIty of Engineering and Information Technology University of Technology Sydney Sydney Australia Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

ISBN: (纸本)9781509006274

We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-time mental assessment. In the literature, EEG signals are severely blended with stochastic noise; therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-the-arts models.

关键词： Electroencephalography Fatigue Real-time systems Vehicles Fuzzy neural networks Signal processing algorithms Integrated circuits

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Hierarchical model predictive control in fusion reactors

Hierarchical model predictive control in fusion reactors

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Proceedings of the Biannual World Automation Congress

作者： I. Garrido S. Coda H.B. Le J.M. Moret V. Queral G. Sevillano A.J. Garrido Department of Automatic Control and Systems Engineering University of the Basque Country (UPV/EHU) Spain Centrede Recherches en Physique des Plasmas Ecole polytechnique fédérale de Lausanne (EPFL) Lausanne Switzerland Laboratorio Nacional de Fusión CIEMAT Spain

Nuclear fusion still has to pass several milestones on its way to successful energy production, one of them being successful plasma stabilization. The present article presents an innovative control implementation related to plasma control for the generation of electricity with magnetically confined plasma. The implementation has been carried out over a specific device of tokamak type, called Tokamak à Configuration Variable (TCV). This novel hybrid control design allows for real time implementation of an optimal model predictive control over a large scale complex system with small time constant. At each closed loop iteration, the full model is first controlled by a straightforward controller, then the output values are used for model reduction so that finally the discretized control system is optimized only for the variables of interest. In the case of the TCV, this novel hybrid model predictive control enhances the power availability on the actuators and extends the pulse duration. Thus, this control strategy will help to achieve fusion energy stated goal of “ignition”, where nuclear fusion generates as much energy as it is needed to start the reaction.

关键词： Magnetic confinement Resistance heating

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Extremum seeking of dynamical systems via gradient descent and stochastic approximation methods

Extremum seeking of dynamical systems via gradient descent a...

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作者： Khong, Sei Zhen Tan, Ying Manzie, Chris Nešić, Dragan Department of Automatic Control Lund University LundSE-221 00 Sweden Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Mechanical Engineering University of Melbourne ParkvilleVIC3010 Australia

This paper examines the use of gradient based methods for extremum seeking control of possibly infinite-dimensional dynamic nonlinear systems with general attractors within a periodic sampled-data framework. First, discrete-time gradient descent method is considered and semi-global practical asymptotic stability with respect to an ultimate bound is shown. Next, under the more complicated setting where the sampled measurements of the plant's output are corrupted by an additive noise, three basic stochastic approximation methods are analysed;namely finite-difference, random directions, and simultaneous perturbation. Semi-global convergence to an optimum with probability one is established. A tuning parameter within the sampled-data framework is the period of the synchronised sampler and hold device, which is also the waiting time during which the system dynamics settle to within a controllable neighbourhood of the steady-state input-output behaviour. © 2015 Elsevier Ltd. All rights reserved.

关键词： Additive noise

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Tracking of wireless mobile nodes in he presence of unknown path-loss characteristics

Tracking of wireless mobile nodes in he presence of unknown ...

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International Conference on Information Fusion

作者： M. W. Khan A. H. Kemp N. Salman L. S. Mihaylova School of Electronic and Electrical Engineering University of Leeds UK Department of Automatic Control and Systems Engineering University of Sheffield UK

Due to the difficult characterization of the propagation model, most studies on racking of mobile nodes assume the correct knowledge of the power-distance gradients or the path-loss exponents (PLEs). In this paper, we first investigate the impact of erroneous PLEs on positioning of a wireless nodes when both distance and bearing measurements are available. Thus, an analytical expression of the mean square error (MSE) in location estimation is derived in case of erroneous PLEs. Second, we propose a novel online PLE estimation and tracking algorithm in dynamic environments. The proposed algorithm estimates the PLE of individual links a every time-step using he generalized pattern search (GenPS) algorithm. The PLE estimates update the observation vector which is used in a Kalman filter (KF) and a particle filter (PF) for tracking. Simulation results show that the racking performance degrades drastically with an incorrect assumption for the PLE values. Further simulations show that tracking with PLE estimation performs considerably beer compared to tracking with incorrectly assumed PLEs.

关键词： Estimation Target tracking Heuristic algorithms Wireless sensor networks Wireless communication Mobile nodes

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Computing finite abstractions with robustness margins via local reachable set over-approximation

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IFAC-PapersOnLine 2015年第27期48卷 1-6页

作者： Li, Yinan Liu, Jun Ozay, Necmiye Department of Automatic Control and System Engineering University of Sheffield SheffieldS1 3JD United Kingdom Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

This paper proposes a method to compute finite abstractions that can be used for synthesizing robust hybrid control strategies for nonlinear systems. Most existing methods for computing finite abstractions utilize some global, analytical function to provide bounds on the reachable sets of nonlinear systems, which can be conservative and lead to spurious transitions in the abstract systems. This problem is even more pronounced in the presence of imperfect measurements and modelling uncertainties, where control synthesis can easily become infeasible due to added spurious transitions. To mitigate this problem, we propose to compute finite abstractions with robustness margins by over-approximating the local reachable sets of nonlinear systems. We do so by linearizing the nonlinear dynamics into linear affine systems and keeping track of the linearization error. It is shown that this approach provides tighter approximations and several numerical examples are used to illustrate of effectiveness of the proposed methods. © 2015, IFAC (International Federation of automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Robustness (control systems) Abstracting Computation theory Nonlinear systems Numerical methods Temporal logic Uncertainty analysis Abstract systems Analytical functions control synthesis Finite abstraction Hybrid control strategies Linearization errors Reachable set Robustness margins

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Optimal scheduling of diesel generators in offshore support vessels to minimize fuel consumption 41

Optimal scheduling of diesel generators in offshore support ...

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41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015

作者： Rao, K. Srinivasa Chauhan, Priyesh J. Panda, Sanjib K. Wilson, Gary Liu, Xiong Gupta, Amit Kumar Department of Electrical and Computer Engineering National University of Singapore Singapore Electrical Power and Control Systems Rolls-Royce Singapore Pte. Ltd. Singapore

ISBN: (纸本)9781479917624

This paper presents optimal generation scheduling for minimization of fuel consumption (FC) in an offshore support vessel with a dynamic positioning system and diesel generator (DG) sets of 8000 kW total capacity. FC is analyzed for equal load sharing among equal capacity DGs, optimal load sharing among equal capacity DGs, and optimal load sharing among unequal capacity DGs. Due to nonlinear specific FC (SFC) curves of the diesel engines, a Genetic Algorithm is used for optimal load sharing. The SFC-vs-load relationship obtained from Brake SFC (BSFC) map of the diesel engines is characterized using a cubic spline interpolation. A solution to the problem is attained by comparing yearly fuel consumption in the three cases for the load profile of target vessel. This method can be applied to any vessel equipped with multiple generating units. A vessel with proper selection of DG capacities and closed bus-tie can achieve maximum fuel savings;however, when deciding the DG capacity and load scheduling, it is necessary to consider fuel consumption alongside concerns such as optimizing exhaust emissions after-treatment, ensuring adequate power availability for essential services, and scheduling engine maintenance. © 2015 IEEE.

关键词： Diesel engines

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