scholarly journals An Identification Method for a State Space Model Set with Time-Varying Parametric Uncertainties by Using the Subspace Method

1999 ◽  
Vol 35 (6) ◽  
pp. 741-747
Author(s):  
Ryozo NAGAMUNE ◽  
Shigeru YAMAMOTO
Keyword(s):  
State Space ◽  
State Space Model ◽  
Parametric Uncertainties ◽  
Time Varying ◽  
Subspace Method ◽  
Space Model ◽  
Identification Method ◽  
Model Set

Analysis of Operating Deflection Shapes Based on Subspace Method

1997 ◽  
Author(s):  
Nobutaka Tsujiuchi ◽  
Yuichi Matsumura ◽  
Takayuki Koizumi
Keyword(s):  
Experimental Data ◽  
Time Domain ◽  
State Space Model ◽  
Measurement Data ◽  
Time Varying ◽  
Subspace Method ◽  
Identification Scheme ◽  
Space Model ◽  
Time Varying Systems ◽  
Frequency Components

Abstract In this paper, we propose the new method to identify the Operating Deflection Shapes (ODSs) from the measurement data of time domain. At first, we present the identification scheme of ODSs based on a state-space model. Then the scheme is extended to identify the ODSs adaptively for the time-varying systems by using the URV Decomposition (URVD). Proposed scheme is able to decompose the deformation of a structure under operating condition into the underlying superposition of well excited frequency components. This paper introduces the algorithm and shows the effectiveness of our proposed scheme applyed for both synthesized and experimental data.

Estimating Time-Varying Networks With a State-Space Model

2020 ◽  
Author(s):  
Shaowen Liu ◽  
Massimiliano Caporin ◽  
Sandra Paterlini
Keyword(s):  
State Space ◽  
State Space Model ◽  
Time Varying ◽  
Space Model ◽  
Time Varying Networks

Path Flow Estimation Using Time Varying Coefficient State Space Model

2009 ◽  
Author(s):  
Yow-Jen Jou ◽  
Chien-Lun Lan ◽  
George Maroulis ◽  
Theodore E. Simos
Keyword(s):  
State Space ◽  
State Space Model ◽  
Time Varying ◽  
Flow Estimation ◽  
Space Model ◽  
Varying Coefficient ◽  
Path Flow Estimation

scholarly journals Extended Application Of Time-Varying Observer For Rigid Rotors Unbalance Estimation During Acceleration

2018 ◽  
Vol 167 ◽  
pp. 02015
Author(s):  
Xunxing Yu ◽  
Kuanmin Mao ◽  
Yaming Zhu
Keyword(s):  
State Space ◽  
Control Strategy ◽  
State Space Model ◽  
Time Varying ◽  
Rigid Rotor ◽  
Space Model ◽  
Active Balancing ◽  
Extended Application ◽  
Balancing Control ◽  
Rigid Rotors

Unbalance is one of essential problems for modern rotating machines. In this work, an improved time-varying observer is proposed to estimate the unbalance of rigid rotor during acceleration. In order to fitting different speed acceleration laws, the unbalance forces have been included in an new designed augmented states, meanwhile the state space model of rigid rotor has been also developed. The developed state space model is transformed to a canonical transformation and a new designed time-varying observer can be obtained. The estimated unbalances can be directly obtained by using this time-varying observer. This method would be very helpful for active balancing control strategy during acceleration.

A State Space Thermal Model for HEV/EV Non-Linear and Time-Varying Battery Thermal Systems

2011 ◽  
Author(s):  
Xiao Hu ◽  
Shaohua Lin ◽  
Scott Stanton ◽  
Wenyu Lian
Keyword(s):  
State Space ◽  
Thermal Management ◽  
State Space Model ◽  
Time Varying ◽  
Cfd Model ◽  
Thermal Systems ◽  
Space Model ◽  
Battery Thermal Management ◽  
Non Linear ◽  
Electrical Vehicle

Battery thermal management for high power applications such as electrical vehicle (EV) or hybrid electrical vehicle (HEV) is crucial. Modeling is an indispensable tool to help engineers design better battery cooling systems. While computational fluid dynamics (CFD) has been used quite successfully for battery thermal management, CFD models can be too large and too slow for repeated transient thermal analysis, especially for a battery module or pack. A state space model based on CFD results can be used to replace the original CFD model. The state space model runs approximately two orders of magnitude faster and yet under some conditions obtains equivalent results as the original CFD model. The state space model is based on linear and time-invariant (LTI) system theory. The main limitation of the method is that the method applies strictly speaking to systems that satisfy both linearity and time invariance conditions. General battery cooling problems unfortunately do not strictly satisfy those two conditions. This paper examines quantitatively the amount of error involved if these two conditions are not met. It turns out that these conditions can be relaxed in some ways while preserving satisfactory results for non-linear and time-varying battery thermal systems. This paper also discusses non-linear curve fitting needed for the method.

An Effective System Identification Method of Small-Scale Unmanned Helicopter

2014 ◽  
Vol 598 ◽  
pp. 442-452
Author(s):  
Yu Zhu Liu ◽  
Fei Hu
Keyword(s):  
Simulated Annealing ◽  
State Space ◽  
Prediction Error ◽  
Simulated Annealing Algorithm ◽  
State Space Model ◽  
Small Scale ◽  
Unmanned Helicopter ◽  
Space Model ◽  
Identification Method ◽  
Error Method

In order to control an unmanned helicopter accurately and reliably, it is necessary to have a precise mathematical model of its dynamics. This paper presents a new timedomain identification method and process for full state space model of small-scale unmanned helicopters. The identification method is called ISAcwPEM (Improved Simulated Annealing combined with Prediction Error Method), which is not sensitive to initial point selection and doesn’t require frequency-sweeping inputs. Firstly, the primary parameters to be identified are selected by model sensitivity analysis. After that, the improved simulated annealing algorithm runs in a distributed computing platform to figure out a 13-order state space model of the SJTU T-REX700E small-scale unmanned helicopter (consisting of a cruise modal and a hover modal). Then the iterative Prediction Error Method (PEM) is used to optimize the model. In addition, the time-delay term and the trim term are estimated and added to the model. Finally, the effectiveness of the identification method is well validated by real outdoor flight experimental results.

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