• Title, Summary, Keyword: state-vector

Search Result 872, Processing Time 0.052 seconds

Direct Stator Flux Vector Control Strategy for IPMSM using a Full-order State Observer

  • Yuan, Qingwei;Zeng, Zhiyong;Zhao, Rongxiang
    • Journal of Electrical Engineering and Technology
    • /
    • v.12 no.1
    • /
    • pp.236-248
    • /
    • 2017
  • A direct stator flux vector control scheme in discrete-time domain is proposed in this paper for the interior permanent magnet synchronous motor (IPMSM) drive to remove the proportional-integral (PI) controller from the direct torque control (DTC) scheme applied to IPMSM and to obtain faster dynamic response and lower torque ripple output. The output of speed outer loop is used as the desired torque angle instead of the desired torque in the proposed scheme. The desired stator flux vector in dq coordinate is calculated with a given amplitude. The state-space equations in discrete-time for IPMSM are established, the actual stator flux vector is estimated in deadbeat manner by a full-order state observer, and then the closed-loop control is achieved by the pole placement. The stator flux error vector is utilized to calculate the reference stator voltage vector. Extracting the angle position and amplitude from the estimated stator flux vector and estimating the output torque are eliminated for the direct feedback control of the stator flux vector. The proposed scheme is comparatively investigated with a PI-SVM DTC scheme by experiment results. Experimental results show the feasibility and advantages of the proposed control scheme.

Generation of Pattern Classifiers Based on Linear Nongroup CA

  • Choi, Un-Sook;Cho, Sung-Jin;Kim, Han-Doo
    • Journal of Korea Multimedia Society
    • /
    • v.18 no.11
    • /
    • pp.1281-1288
    • /
    • 2015
  • Nongroup Cellular Automata(CA) having two trees in the state transition diagram of a CA is suitable for pattern classifier which divides pattern set into two classes. Maji et al. [1] classified patterns by using multiple attractor cellular automata as a pattern classifier with dependency vector. In this paper we propose a method of generation of a pattern classifier using feature vector which is the extension of dependency vector. In addition, we propose methods for finding nonreachable states in the 0-tree of the state transition diagram of TPMACA corresponding to the given feature vector for the analysis of the state transition behavior of the generated pattern classifier.

L1-norm Regularization for State Vector Adaptation of Subspace Gaussian Mixture Model (L1-norm regularization을 통한 SGMM의 state vector 적응)

  • Goo, Jahyun;Kim, Younggwan;Kim, Hoirin
    • Phonetics and Speech Sciences
    • /
    • v.7 no.3
    • /
    • pp.131-138
    • /
    • 2015
  • In this paper, we propose L1-norm regularization for state vector adaptation of subspace Gaussian mixture model (SGMM). When you design a speaker adaptation system with GMM-HMM acoustic model, MAP is the most typical technique to be considered. However, in MAP adaptation procedure, large number of parameters should be updated simultaneously. We can adopt sparse adaptation such as L1-norm regularization or sparse MAP to cope with that, but the performance of sparse adaptation is not good as MAP adaptation. However, SGMM does not suffer a lot from sparse adaptation as GMM-HMM because each Gaussian mean vector in SGMM is defined as a weighted sum of basis vectors, which is much robust to the fluctuation of parameters. Since there are only a few adaptation techniques appropriate for SGMM, our proposed method could be powerful especially when the number of adaptation data is limited. Experimental results show that error reduction rate of the proposed method is better than the result of MAP adaptation of SGMM, even with small adaptation data.

Comparison of Sediment Yield by IUSG and Tank Model in River Basin (하천유역의 유사량의 비교연구)

  • Lee, Yeong-Hwa
    • Journal of Environmental Science International
    • /
    • v.18 no.1
    • /
    • pp.1-7
    • /
    • 2009
  • In this study a sediment yield is compared by IUSG, IUSG with Kalman filter, tank model and tank model with Kalman filter separately. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. In the IUSG with Kalman filter, the state vector of the watershed sediment yield system is constituted by the IUSG. The initial values of the state vector are assumed as the average of the IUSG values and the initial sediment yield estimated from the average IUSG. A tank model consisting of three tanks was developed for prediction of sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. A tank model with Kalman filter is developed for prediction of sediment yield. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error.

A DCT-based hierarcical finite state vector quantization for image coding (영상 부호화를 위한 이산 여현변환 기반의 계층적 유한 상태 벡터 양자화 기법)

  • 남일우;김응성;이근영
    • Journal of the Korean Institute of Telematics and Electronics S
    • /
    • v.35S no.1
    • /
    • pp.88-95
    • /
    • 1998
  • In this paper, we introduce a new DCT based hierarchical finite state vector quantization. Our proposed scheme uses difference of DCT coefficients to find a representative vector, and classifies image blocks into different hierarchical levels depending on their structural characteristics, and uses different coding rates and different number os state codebooks at each hierarchical levels. As a result, we obtained reconstructed images having satisfiable quality objectively.

  • PDF

A Study Nuenal Model of Concept Retrieval (개념 검색의 신경회로망 모델에 관한 연구)

  • Kauh, Yong-Hoon;Park, Sang-Hui
    • Proceedings of the KIEE Conference
    • /
    • /
    • pp.450-456
    • /
    • 1990
  • In this paper, production system is implemented with the inferential neural network model using semantic network and directed graph. Production system can be implemented with the transform of knowledge representation in production system into semantic network and of semantic network into directed graph, because directed graphs can be expressed by neural matrices. A concept node should be defined by the state vector to calculated the concepts expressed by matrices. The expressional ability of neunal network depends on how the state vector is defined. In this study, state vector is overlapped and each overlapping part acts as a inheritant of concept.

  • PDF

Map Building and Localization Based on Wave Algorithm and Kalman Filter

  • Saitov, Dilshat;Choi, Jeong Won;Park, Ju Hyun;Lee, Suk Gyu
    • IEMEK Journal of Embedded Systems and Applications
    • /
    • v.3 no.2
    • /
    • pp.102-108
    • /
    • 2008
  • This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.

  • PDF

Adaptive States Feedback Control of Unknown Dynamics Systems Using Support Vector Machines

  • Wang, Fa-Guang;Kim, Min-Chan;Park, Seung-Kyu;Kwak, Gun-Pyong
    • Journal of information and communication convergence engineering
    • /
    • v.6 no.3
    • /
    • pp.310-314
    • /
    • 2008
  • This paper proposes a very novel method which makes it possible that state feedback controller can be designed for unknown dynamic system with measurable states. This novel method uses the support vector machines (SVM) with its function approximation property. It works together with RLS (Recursive least-squares) algorithm. The RLS algorithm is used for the identification of input-output relationship. A virtual state space representation is derived from the relationship and the SVM makes the relationship between actual states and virtual states. A state feedback controller can be designed based on the virtual system and the SVM makes the controller with actual states. The results of this paper can give many opportunities that the state feedback control can be applied for unknown dynamic systems.

A Linear Reservoir Model with Kslman Filter in River Basin (Kalman Filter 이론에 의한 하천유역의 선형저수지 모델)

  • 이영화
    • Journal of Environmental Science International
    • /
    • v.3 no.4
    • /
    • pp.349-356
    • /
    • 1994
  • The purpose of this study is to develop a linear reservoir model with Kalman filter using Kalman filter theory which removes a physical uncertainty of :ainfall-runoff process. A linear reservoir model, which is the basic model of Kalman filter, is used to calculate runoff from rainfall in river basin. A linear reservoir model with Kalman filter is composed of a state-space model using a system model and a observation model. The state-vector of system model in linear. The average value of the ordinate of IUH for a linear reservoir model with Kalman filter is used as the initial value of state-vector. A .linear reservoir model with Kalman filter shows better results than those by linear reserevoir model, and decreases a physical uncertainty of rainfall-runoff process in river basin.

  • PDF

The Gentan Probability, A Model for the Improvement of the Normal Wood Concept and for the Forest Planning

  • Suzuki, Tasiti
    • Journal of Korean Society of Forest Science
    • /
    • v.67 no.1
    • /
    • pp.52-59
    • /
    • 1984
  • A Gentan probability q(j) is the probability that a newly planted forest will be felled at age-class j. A future change in growing stock and yield of the forests can be predicted by means of this probability. On the other hand a state of the forests is described in terms of an n-vector whose components are the areas of each age-class. This vector, called age-class vector, flows in a n-1 dimensional simplex by means of $n{\times}n$ matrices, whose components are the age-class transition probabilities derived from the Gentan probabilities. In the simplex there exists a fixed point, into which an arbitrary forest age vector sinks. Theoretically this point means a normal state of the forest. To each age-class-transition matrix there corresponds a single normal state; this means that there are infinitely many normal states of the forests.

  • PDF