scholarly journals Parameters Estimation of Interharmonic Based on State Space Model

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Yuan Shiji ◽  
Pei Bin ◽  
Liu Zhihua ◽  
Huang Wenjing ◽  
Sun Mingfeng
Keyword(s):  
State Space ◽  
Gaussian Noise ◽  
State Space Model ◽  
Hankel Matrix ◽  
Parameters Estimation ◽  
White Gaussian Noise ◽  
Joint Estimation ◽  
Superior Performance ◽  
Space Model ◽  
Simulation Results

The special Hankel matrix is structured from interharmonic sampling, which is described by state space model. A method of parameters estimation based on state space model is proposed, which can achieve interharmonics frequency, amplitude and, phase of the joint estimation. The simulation results show that the method can effectively restrain white Gaussian noise, with superior performance.

State-space Model Identification Using Input and Output Data With Steady State Values Zeroing Multiple Integrals of Output Error

2005 ◽  
Vol 128 (3) ◽  
pp. 746-749
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Eiichi Bamba ◽  
Hiroshi Shibata
Keyword(s):  
Steady State ◽  
System Identification ◽  
State Space ◽  
State Space Model ◽  
Hankel Matrix ◽  
Output Data ◽  
Single Input Single Output ◽  
Space Model ◽  
Output Error ◽  
Input And Output

This study proposes a new deterministic off-line identification method that obtains a state-space model using input and output data with steady state values. This method comprises of two methods: Zeroing the 0∼N-tuple integral values of the output error of single-input single-output transfer function model (Kosaka et al., 2004) and Ho-Kalman’s method (Zeiger and McEwen, 1974). Herein, we present a new method to derive a matrix similar to the Hankel matrix using multi-input and multi-output data with steady state values. State space matrices A, B, C, and D are derived from the matrix by the method shown in Zeiger and McEwen, 1974 and Longman and Juang, 1989. This method’s utility is that the derived state-space model is emphasized in the low frequency range under certain conditions. Its salient feature is that this method can identify use of step responses; consequently, it is suitable for linear mechanical system identification in which noise and vibration are unacceptable. Numerical simulations of multi-input multi-output system identification are illustrated.

Research on Control System of Pump and Valve Parallel Connection

2010 ◽  
Vol 44-47 ◽  
pp. 1751-1757 ◽  
Author(s):  
Jing Luo ◽  
Chun Geng Sun ◽  
Peng Zhang ◽  
Sen Liu ◽  
Song Tao Wu
Keyword(s):  
Control System ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Parallel Connection ◽  
Space Model ◽  
Valve System ◽  
Simulation Results ◽  
Valve Control

This paper introduces the composition of pump and valve control system of parallel connection and the output flux of pump and valve is distributed optimally. Then, establish the state-space model of pump and valve system, the system is simulated by Simlink and AMESim software, the simulation results obtained.

scholarly journals Modal Parameters Estimation of Building Structures from Vibration Test Data Using Observability Measurement

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jae-Seung Hwang ◽  
Hongjin Kim ◽  
Bong-Ho Cho
Keyword(s):  
Experimental Data ◽  
State Space ◽  
Shaking Table Test ◽  
State Space Model ◽  
Parameters Estimation ◽  
Shaking Table ◽  
Mode Shapes ◽  
Vibration Test ◽  
Space Model ◽  
Participation Factor

The load distribution to each mode of a structure under seismic loading depends on the modal participation factors and mode shapes and thus the exact estimation of modal participation factors and mode shapes is essential to analyze the seismic response of a structure. In this study, an identification procedure for modal participation factors and mode shapes from a vibration test is proposed. The modal participation factors and mode shapes are obtained from the relationship between observability matrices realized from the system identification. Using the observability matrices, it is possible to transform an arbitrarily identified state space model obtained from the experimental data into a state space model which is defined in a domain with physical meaning. Then, the modal participation factor can be estimated based on the transformation matrix between two state space models. The numerical simulation is performed to evaluate the proposed procedure, and the results show that the modal participation factor and mode shapes are estimated from the structural responses accurately. The procedure is also applied to the experimental data obtained from the shaking table test of a three-story shear building model.

scholarly journals State space model realization using step response data of MIMO system with input delays for model predictive control

2019 ◽  
pp. 93-104
Author(s):  
Mohammed Jimoh ◽  
Ado Dan’Isa
Keyword(s):  
State Space ◽  
Predictive Control ◽  
Internal Model ◽  
State Space Model ◽  
Hankel Matrix ◽  
Step Response ◽  
Response Model ◽  
Space Model ◽  
Response Data ◽  
Input Delays

This paper discusses state space realization algorithm from general multiple input multiple output (MIMO) step response data with or without input delays. It uses the factored form of the block Hankel matrix formed from the Markov parameters of the equivalent impulse response of the step response model to obtain an equivalent state space model. Though the state space model that best approximates the step response model has number of states equal to the rank of the block Hankel matrix, much lower rank state space model, adequate for use as internal model for model predictive control (MPC), can also be obtained. By using an empirical step response model of a pilot distillation plant, a simple state space MPC, which use a full rank, and then a low-rank approximation of the model as its internal model, is implemented on the plant model. The setpoint tracking trends of the control outputs of the two approximations match closely.

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