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27th IASTED International Conference on Modelling, Identification, and control

作者： Hirmajer, T. Fikar, M. Balsa-Canto, E. Banga, J.R. Institute of Information Engineering Automation and Mathematics Department of Information Engineering and Process Control Faculty of Chemical and Food Technology STU Radlinského 9 812 37 Bratislava Slovakia Spanish Council for Scientific Research C Eduardo Cabello 6 36208 Vigo Spain

ISBN: (纸本)9780889867116

This paper explores the possibilities of using the combination of the state of the art nonlinear programming problem solver IPOPT and the initial value problem solver CVODES (incorporated in SUNDIALS) for the solution of dynamic optimization problems, also regarded as open loop optimal control problems (OCP). Recent works confirm the potential of IPOPT in solving large scale optimization problems and particularly in solving optimal control problems in the framework of the complete parameterization approach [12]. Here the OCP problems are solved using the control vector parameterization scheme and the advantages and disadvantages of the combination CVP + IPOPT + CVODES are evaluated through several examples.

关键词： Ordinary differential equations

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New insights into derivative estimation via least squares approximation - theory and application

New insights into derivative estimation via least squares ap...

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2008 American control Conference (ACC), vol.6

作者： Philipp Mai Claus Hillermeier Faculty of Electrical Engineering Institute of Automation and Control Universitat der Bundeswehr München Neubiberg Germany

In this article, we revise a well-known derivative estimation scheme which is based on a least squared error polynomial approximation of a noisy measurement signal. Our contribution is to determine the influence of the estimation parameters onto the covariance matrix and the temporal delay of the estimation result. Also, it is shown that the least squares estimator is statistically optimal in the presence of white gaussian measurement noise. Our ideas are applied to the estimation of derivatives of the first state of Chen's Chaotic Oscillator and to the fault tolerant swing up of the Inverted Pendulum on a Cart.

关键词： Least squares approximation Delay estimation Polynomials Parameter estimation Covariance matrix Noise measurement White noise Gaussian noise State estimation Chaos

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Hippocampal-like categorization of object views: A self-organizing learning approach to vision modeling using stochastic grammar inference and associative memory

Hippocampal-like categorization of object views: A self-orga...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Peter Michael Goebel Markus Vincze Bernard Favre-Bulle ACIN Automation & Control Institute Faculty of Electrical Engineering and Information Technology University of Technology Vienna Vienna Austria

The categorization problem in object recognition is the assignment of semantic categories to objects or parts of objects. Here, the best categorization performance provide mammalian brain functions that motivate to partly mimic such a formation for the application to machine learning and automation. Curiosity and experimental willingness support human recognizing and detecting in relation to still unknown objects. In order to learn an understanding of the topology of such objects, objects are observed from a series of different viewpoints. This results in a collection of object views that afterward can be used by cognitive processes. State-of-the-art systems provide learning structures that are subsequently utilized for object recognition and tracking tasks. However, most of these systems aim at very specific goals in restricted domains, and therefore, it remains no room for learning and understanding of object structures. In the work of this paper, we propose a new robot vision model for neural categorization. This is close to our initial idea of mimicking mammalian brain functions for robot vision. We use the combinatorial solution of our cognitive framework with an embedding of a recently presented stochastic n-gram model, supported by a three-dimensional grammar model on a discrete three-dimensional lattice. Furthermore, we use ant colony optimization heuristics for collecting the transition probabilities of the n-gram model. The proposed solution is exemplified by applying the method to several object view series of 69 polytopes generated out of the five platonic solids by truncation; thus, generating images from 32 viewpoints each, yields an object set of 2208 images. These set is further expanded to four sets perturbed by Gaussian-noise with varying sigma = {0, 0.1, 0.5, 1, 2}. Finally, we show results for selected objects and conclude with an outlook on further work.

关键词： Junctions Lattices Stochastic processes Brain modeling Brain models Solid modeling Ant colony optimization

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Automatic Sleep Stage Determination by Multi-Valued Decision Making Based on Conditional Probability with Optimal Parameters

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SICE Journal of control, Measurement, and System Integration 2008年第6期1卷 423-428页

作者： Bei Wang Takenao Sugi Xingyu Wang Masatoshi Nakamura Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga UniversityDepartment of Automation School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Electronic Engineering Faculty of Science and Engineering Saga University Department of Automation School of Information Science and Engineering East China University of Science and Technology Department of Advanced Systems Control Engineering Graduate School of Science and Engineering Saga University

Data for human sleep study may be affected by internal and external influences. The recorded sleep data contains complex and stochastic factors, which increase the difficulties for the computerized sleep stage determination techniques to be applied for clinical practice. The aim of this study is to develop an automatic sleep stage determination system which is optimized for variable sleep data. The main methodology includes two modules: expert knowledge database construction and automatic sleep stage determination. Visual inspection by a qualified clinician is utilized to obtain the probability density function of parameters during the learning process of expert knowledge database construction. Parameter selection is introduced in order to make the algorithm flexible. Automatic sleep stage determination is manipulated based on conditional probability. The result showed close agreement comparing with the visual inspection by clinician. The developed system can meet the customized requirements in hospitals and institutions.

关键词： automatic sleep stage determination visual inspection multi-valued decision making parameter selection conditional probability

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Subspace identification methods and fMRI analysis

Subspace identification methods and fMRI analysis

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jana Tauchmanova Martin Hromcik Department of Control Engineering Faculty of Electrical Engineering Institute of Information Theory and Automation Czech Technical University Prague Czech Republic Center for Applied Cybernetics Faculty of Electrical Engineering Institute of Information Theory and Automation Czech Technical University Prague Czech Republic

The main goal of this paper is to propose application of modern multidimensional systems identification algorithms of the subspace identification theory in the context of fMRI data analysis. The methods originated in 1990s in the field of process control and identification and yield robust linear model parameter estimates for systems with many inputs, outputs and states. Our ultimate goal is to establish an alternative to the DCM analysis procedure which would eliminate its main drawbacks, namely the need to pre-define the models structure. The paper discusses results based on simulated data provided by the DCM simulator in the SPM toolbox. Several scenarios are presented, with varying amount of noise and number of data samples.

关键词： Data models Functional magnetic resonance imaging Brain modeling Signal to noise ratio Analytical models Mathematical model Process control

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Single-phase optimal Odd PWM problem

Single-phase optimal Odd PWM problem

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Annual Conference of Industrial Electronics Society

作者： Petr Kujan Martin Hromcik Michael Sebek Department of Control Engineering FEEInstitute of Information Theory and Automation of the ASCR Czech Technical University of Prague Center for Applied Cybernetics Faculty of Electrical Engineering Czech Technical University of Prague Department of Control Engineering Faculty of Electrical Engineering Czech Technical University of Prague

Optimal pulse width modulation (PWM) problem is an established method of generating PWM waveforms with low baseband distortion. In this paper we focused on computation of optimal switching angles of a odd PWM waveform for generating general odd symmetric waveforms. We introduce an exact and fast algorithm with the complexity of only O(n log 2 n) arithmetic operations. This algorithm is based on transformation of appropriate trigonometric equations for each harmonics to a polynomial system of equations that is transferred to a special system of sum of powers. The solution of this system is carried out by modification of Newtonpsilas identity via Pade approximation and orthogonal polynomials theory and property of symmetric polynomials. Finally, the optimal switching sequence is obtained by computing the zeros of two orthogonal polynomials in one variable.

关键词： Pulse width modulation Polynomials Baseband Equations Power harmonic filters Chebyshev approximation control engineering Space vector pulse width modulation Pulse generation Pulse width modulation inverters

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Modelling and identification of a small unmanned aerial vehicle

Modelling and identification of a small unmanned aerial vehi...

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作者： Soumelidis, Alexandres Gaspar, Péter Bauer, Péter Balas, Gary Systems and Control Lab Computer and Automation Research Institute Hungarian Academy of Sciences Kende st 13-17 Budapest H-1111 Hungary Dept. of Control and Transport Automation Faculty of Transportation Engineering Budapest University of Technology and Economics Bertalan Lajos st 2 Budapest H-1111 Hungary Dept. of Aerospace Engineering and Mechanics Institute of Technology University of Minnesota 110 Union st SE Minneapolis MN 55455-0153 United States

ISBN: (纸本)9789633130117

The field of Unmanned Aerial Vehicles (UAV) has gained great significance in the R&D activities of several institutions, and numerous realizations have been constructed. controlling the movement of a UAV is one of the most significant tasks in the hierarchical control system that should be realized in order to successfully manage UAVs in several missions. Precise control of the movement requires accurate mathematical models of the aircraft that correspond to the several control schemes that are intended to apply, including linearised, LPV, and nonlinear ones. The source of mathematical models includes the physical model constructed on the basis of the Newtonian equations and the rules of aerodynamics, as well as the use of empirical observations, measurements. The methodology of system identification offers the tools that can be used to manage this field. The Systems and control Laboratory of the Computer and automation Research Institute in cooperation with University of Minnesota and Budapest University of Technology and Economics has built - as a part of a more general framework established to solve complex UAV control problems - small UAVs, equipped with on-board sensors and embedded computer with the purpose of obtaining an adequate test platform for the aircraft experiments. Based on on-board measurements acquired during the flight tests performed with the UAVs, the opportunity has been given to test several system identification approaches corresponding to the control tasks to be solved. This paper gives an outline of the system identification and modelling methods that have successfully been applied, and also analyzes the problems encountered in the data-acquisition - signal processing - computing chain.

关键词： Unmanned aerial vehicles (UAV)

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Nonlinear identification and MIMO control of a laboratory helicopter using genetic algorithm

Nonlinear identification and MIMO control of a laboratory he...

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IEEE India Conference (INDICON)

作者： H. Tahersima A. Fatehi Advanced Process Automation & Control (APAC) research group Faculty of Electrical Engineering Khaje Nasir Toosi University of Technology Tehran Iran

Closed loop identification of nonlinear model and control of a laboratory helicopter using genetic algorithm is proposed in this paper. The derived model has a nonlinear structure. Using the previous results of the physical modeling of the studied plant, a nonlinear model is considered based on the physical dynamics of the system. However, there is no need to perform numerous physical experiments to estimate the model parameters. Instead, genetic algorithm as a nonlinear optimization technique is used to obtain the parameters of the model. Therefore, the advantage of both modeling and identification methods are employed. In the next step, the parameters of a multi input-multi output (MIMO) PID controller for the derived model will be tuned by GA using the obtained nonlinear model as a simulator of the plant. Applying the controller to both the real plant and the simulation model, the accuracy of the model and the performance of the controller is examined. The results demonstrate that the achieved model accurately fits to the behavior of the real plant and the controller designed based on this model, can control the real system appropriately.

关键词： MIMO Laboratories Helicopters Genetic algorithms Parameter estimation Automatic control Mathematical model Electronic mail Nonlinear dynamical systems control system synthesis

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Real-Time Level Plant control Using Improved BELBIC

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IFAC Proceedings Volumes 2008年第2期41卷 4631-4635页

作者： M. Masoudinejad R. Khorsandi A. Fatehi C. Lucas S. Fakhimi Derakhshan M.R. Jamali Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Center of Excellence for Control and Intelligent Processing Electrical and Computer engineering Department University of Tehran Iran

Brain emotional learning based intelligent controller (BELBIC) is based on computational model of limbic system in the mammalian brain. In recent years, this model was applied in many linear and nonlinear control applications. Previous studies show that this controller has fast response, simple implementation and robustness with respect to disturbances. It is also possible to define emotional signal based on control application objectives. But in the previous studies, internal instability of this controller was not considered and control task were done in limited time period. In this article mathematical description of BELBIC is investigated and improved to avoid internal instability. Simulation and implementation of improved model was done on level plant. The obtained results showed that instability of model has been solved in the new model without loss of performance by using Integral Anti Windup (IAW).

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control of a dynamic nonlinear/ uncertain system via a variable structure system approach

Control of a dynamic nonlinear/ uncertain system via a varia...

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International Conference on Optimization of Electrical and Electronic Equipment, OPTIM

作者： F. Moldoveanu J. Vittek S. Ryvkin D. Floroian C. Boldisor Department of Automation Transilvania University Brasov Romania Faculty of Electrical Engineering University of Zilina Slovak Republic Institute of Control Sciences Moscow Russia

In this paper the control problem of nonlinear/ uncertain dynamic systems via a variable structure systems (VSS) approach is discussed. Observation of the states of such systems is also considered. The extension to an... 详细信息

关键词： Observers Switches control systems Trajectory Manipulators Mathematical model Uncertain systems

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