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Subspace identification of Local 1D Homogeneous Systems

IFAC-PapersOnLine 2015年第28期48卷 892-896页

作者： Yu, Chengpu Verhaegen, Michel Hansson, Anders Delft Center for Systems and Control Delft University Delft2628CD Netherlands Division of Automatic Control Department of Electrical Engineering Linkoping University Sweden

This paper studies the local subspace identification of 1D homogeneous networked systems. The main challenge lies at the unmeasurable interconnection signals between neighboring subsystems. Since there are many unknown inputs to the concerned local system, the corresponding identification problem is semi-blind. To cope with this problem, a nuclear norm optimization based subspace identification is presented, which is carried out for solving the Markov parameters of a locally lifted system, followed by determining the system matrices of a single subsystem. In the step of Markov parameter estimation, we form a nuclear norm regularized optimization problem which can well handle the adverse effects of the unknown system inputs as long as the number of unknown system inputs is relatively small. In the step of system realization, we again derive a nuclear norm regularized optimization formulation which can cope with the under-determinedness of the realization problem. In the end, the overall identification algorithm is summarized. © 2015

关键词： Parameter estimation Markov processes identification algorithms identification problem Markov parameters Rank constraints Regularized optimization problems Regularized optimizations Subspace identification System realization

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Inverse Reinforcement Learning for identification in Linear-Quadratic Dynamic Games

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IFAC-PapersOnLine 2017年第1期50卷 14902-14908页

作者： Köpf, Florian Inga, Jairo Rothfuß, Simon Flad, Michael Hohmann, Sören Institute of Control Systems Karlsruhe Institute of Technology Karlsruhe76131 Germany

The theory of dynamic games has received considerable attention in a wide range of fields. While great effort has been made to develop new algorithms for finding Nash equilibria in dynamic games, the identification of cost functions has received little attention. We present an identification algorithm for linear quadratic dynamic games, a framework which can be applied in the field of shared control between a human and an automatic controller. In this application, the cost function describing human behavior is identified, taking into account the influence of the automation. Furthermore, we consider that human movement underlies certain variability by using a probabilistic Inverse Reinforcement Learning approach. As identification is performed in a single optimization step, the proposed method is suited for real-time applications. A simulation example shows that the algorithm successfully identifies the cost function of the first player which—in combination with the second player—reproduces the observed system output. © 2017

关键词： Reinforcement learning Behavioral research Cost functions Costs Game theory identification (control systems) Inverse problems Maximum entropy methods Automatic controllers identification algorithms Inverse reinforcement learning Inverse optimal control Real time application Shared control Simulation example Theory of dynamics

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A Combined Global and Local identification Approach for LPV Systems

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IFAC-PapersOnLine 2015年第28期48卷 184-189页

作者： Turk, Dora Pipeleers, Goele Swevers, Jan Department of Mechanical Engineering KU Leuven Celestijnenlaan 300 - box 2420 3001 Leuven Belgium

This paper tackles the problem of identifying linear parameter-varying (LPV) systems by combining data originating from global and local identification experiments into a nonlinear leastsquares problem. One extreme of the approach results in a model optimal with respect to the system behavior under varying scheduling parameter conditions, while the other gives a model being a good approximation of system behavior for fixed scheduling parameter. When measurements from global and local experiments are available, a compromise between the two objectives is achieved. Numerical and experimental validations, accompanied by comparisons with existing LPV identification methods show the potential of the developed approach. © 2015

关键词： Time domain analysis Frequency estimation Frequency response Nonlinear systems Numerical methods Parameter estimation Scheduling State space methods identification algorithms Optimization problems State space models Sub space methods Time domain response validation

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Shifting Method for Relay Feedback identification

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IFAC-PapersOnLine 2016年第12期49卷 1933-1938页

作者： Hofreiter, M. Department of Instrumentation and Control Engineering Czech Technical University in Prague Faculty of Mechanical Engineering Czech Republic

This paper introduces a method extending possibilities of the relay feedback identification of dynamic systems describable by their transfer function. The proposed “shifting” method enables to estimate three points on a plant frequency response from a single relay feedback test without using the FFT algorithm. The approach enables to estimate up to five parameters of the transfer function from sustained oscillations obtained by one relay test. For this purpose the method uses a biased relay. The introduced technique offers a tool for a large class of common control problems. The applicability of the proposed frequency-response identification method is demonstrated by estimating five parameters of the anisochronic model and by estimating three points on the frequency response of the physical model called “Air Aggregate”. All experiments were performed in Matlab/Simulink programming environment. © 2016

关键词： Transfer functions Feedback Frequency estimation Frequency response MATLAB Relay control systems Estimation parameters identification algorithms oscillation Plant model Relay control

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Biased-Relay Feedback identification for Time Delay Systems

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IFAC-PapersOnLine 2017年第1期50卷 14620-14625页

作者： Hofreiter, M. Department of Instrumentation and Control Engineering Czech Technical University in Prague Faculty of Mechanical Engineering Czech Republic

The paper describes a new technique for estimation up to three points on a process frequency response from a single biased-relay feedback test without using the FFT algorithm. The new technique called “shifting method” extends possibilities of the relay feedback identification of dynamic systems describable by linear models. The introduced identification method is applicable for systems with dead time if there are stable limit cycles in biased-relay feedback tests. This approach enables to fit up to five parameters of a process transfer function from data observed by one relay experiment. The method may be used for parameter estimation of linear mathematical models with various structures and therefore it can be used for parameter fitting both isochronic and anisochronic linear models. This is demonstrated on proportional and integrating time delay processes described by isochronic or anisochronic linear models. The paper also introduces explicit formulas for the parameter estimation of the process model of the second order plus time delay. The applicability of the proposed method is demonstrated on simulated examples. Matlab/Simulink programming environment was used for all simulations. © 2017

关键词： Relay control systems Delay control systems Estimation Feedback Frequency estimation Frequency response MATLAB Three term control systems Time delay Timing circuits Estimation parameters identification algorithms oscillation Process Modeling Relay control

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identification of Static Boundary Model Based on Gaussian Process Classification

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IFAC-PapersOnLine 2016年第11期49卷 787-792页

作者： Oyama, H. Yamakita, M. Sata, K. Ohata, A. Dept. of Mechanical and Control Engineering Tokyo Institute of Technology Tokyo Japan Advanced Unit Management System Development Div. TOYOTA MOTOR CORPORATION Shizuoka Japan TECHNOVA INC. Higashifuji Technical Center Toyota Motor Corporation Shizuoka Japan

Due to the needs for CO2emission reduction, the control region for automotive engine system is pushed toward the boundary between normal and abnormal engine operations such as knocking and misfiring. When an engine is operated outside of admissible region, the engine or the catalyst is seriously damaged or passengers feel very uncomfortable. Therefore the identification of the boundary is highly demanded. In this paper we propose a design of experiment (DoE) strategy based on the Gaussian process classifier (GPC) with the expectation propagation (EP) algorithm. In the experimental result, the proposed algorithm can identify the boundaries of an engine benchmark problem, provided by the joint research committee of the Society of Automotive Engineers (JSAE) and Society of Instrument and Control Engineers (SICE). © 2016

关键词： Gaussian distribution Classification (of information) Design of experiments Emission control Engines Gas emissions Gaussian noise (electronic) Engine control Engine modelling Gaussian Processes identification algorithms Model based controls Statistical design

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A new identification method for dual-rate Hammerstein systems

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IFAC-PapersOnLine 2015年第28期48卷 853-856页

作者： Chen, Jing Jiang, Bin School of Internet of Things Engineering Jiangnan University Wuxi214122 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China

This paper studies a new identification method for dual-rate Hammerstein systems with piece-wise linearities. By using a switching function, the model of the nonlinear Hammerstein systems be changed to an identification model, then based on the derived model, a missing output identification model based stochastic gradient identification algorithm is used to estimate all the unknown parameters of the systems. An example is provided to show the effectiveness of the proposed algorithm. © 2015

关键词： Stochastic models Nonlinear systems Parameter estimation Stochastic systems Dual rates Dual rate systems Hammerstein system identification algorithms identification method identification model Piece wise Stochastic gradient

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An Approximate Algorithm for Quantum Hamiltonian identification with Complexity Analysis

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IFAC-PapersOnLine 2017年第1期50卷 11744-11748页

作者： Wang, Yuanlong Dong, Daoyi Petersen, Ian R. Zhang, Jun School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Centre for Quantum Computation and Communication Technology Australia Research Council CanberraACT2600 Australia Research School of Engineering Australian National University CanberraACT0200 Australia Joint Institute of UM-SJTU Shanghai Jiao Tong University Shanghai200240 China

identification of the Hamiltonian is vital for characterizing the dynamical evolution of a quantum system. The dimension of a multi-qubit system increases exponentially with the qubit number, which usually leads to daunting computational complexity for general Hamiltonian identification algorithms. In this paper, we design an efficient quantum Hamiltonian identification method based on periodical sampling. The computational complexity is O(M2+ MN2), where M is the number of unknown parameters to be identified in the Hamiltonian and N is the length of the sampling data. Numerical results with different data lengths demonstrate the effectiveness of the proposed identification algorithm. © 2017

关键词： Hamiltonians Computational complexity Quantum computers Quantum optics Approximate algorithms Complexity analysis Dynamical evolution identification algorithms identification method Quantum Hamiltonians Quantum system Sampling data

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Subspace-based system identification: weighting and pre-filtering of instruments

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AUTOMATICA 2002年第3期38卷 433-443页

作者： Gustafsson, T Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

Subspace-based system identification is typically based on an estimate of the extended observability matrix. It is thus of great interest to investigate, and also optimize, the estimate of the observability matrix. Of special interest in this paper is the fact that the influence of certain weighting matrices is an unresolved issue in the literature on subspace identification. Here, an asymptotic analysis of the estimated observability matrix is presented. The main result of the analysis is that novel weighting matrices and pre-filters of instrumental variables are derived. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： identification algorithms subspace methods state-space models asymptotic analysis

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Total least squares in fuzzy system identification: An application to an industrial engine

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2008年第8期21卷 1277-1288页

作者： Jakubek, Stefan Hametner, Christoph Keuth, Nikolaus AVL List GmbH A-8020 Graz Austria Vienna Univ Technol Inst Mech & Mechatron A-1040 Vienna Austria

Takagi-Sugeno fuzzy models have proved to be a powerful tool for the identification of nonlinear dynamic systems. Their generic nonlinear model representation is particularly useful if information about the structure of the nonlinearity is available. In view of a practical applicability in industrial applications two important issues are addressed. First, the problem of unbiased estimation of local model parameters in the presence of input and output noise is considered. For that purpose the concept of total least squares for parameter estimation is reviewed and a related partitioning algorithm based on statistical criteria is presented, Second, the steady-state accuracy of dynamic models is addressed. A concept of constrained TLS parameter optimisation is introduced which enforces the adherence of the model to selected steady-state operating points and thus significantly improves the model accuracy during steady-state phases. Results from a simulation model and from an industrial gas engine power plant demonstrate the capabilities of the proposed concepts. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Takagi-Sugeno fuzzy models Nonlinear system identification identification algorithms Total least squares Gas engine Local model networks Steady-state constraints

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