Steady-State and Dynamic State-Space Model for Fast and Efficient Solution and Stability Assessment of ASDs

2011 ◽  
Vol 58 (7) ◽  
pp. 2836-2847 ◽  
Author(s):  
Juan Segundo-Ramirez ◽  
Ernesto Barcenas ◽  
Aurelio Medina ◽  
Victor Cardenas
Keyword(s):  
Steady State ◽  
State Space ◽  
Efficient Solution ◽  
State Space Model ◽  
Dynamic State ◽  
Stability Assessment ◽  
Space Model

A New Dynamic State Space Model for the Cylindrical Grinding Process

2005 ◽  
Author(s):  
Cheol W. Lee
Keyword(s):  
Surface Roughness ◽  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Grinding Process ◽  
Cylindrical Grinding ◽  
Space Model ◽  
Proposed Model ◽  
Grinding Power ◽  
Actual Part

A new dynamic state space model is proposed for the in-process estimation and prediction of part qualities in the plunge cylindrical grinding process. A through review on various grinding models in literature reveals a hidden dynamic relationship among the grinding conditions, the grinding power, the surface roughness, and the part size due to the machine dynamics and the wheel wear, based on which a nonlinear state space equation is derived. After the model parameters are determined according to the reported values in literature, several simulations are run to verify that the model makes good physical sense. Since some of the output variables, such as the actual part size, may or may not be measured in industry applications, the observability is tested for different sets of outputs in order to see how each set of on-line sensors affects the observability of the model. The proposed model opens a new way of estimating the part qualities such as the surface roughness and the actual part size based on application of the state estimation algorithm to the measured outputs such as the grinding power. In addition, a long term prediction of the part qualities in batch grinding processes would be realized by simulation of the proposed model. Possible applications to monitoring and control of grinding processes are discussed along with several technical challenges lying ahead.

Improved State-Space Model Identification Using Input and Output Data with Steady State Values

2004 ◽  
Vol 37 (11) ◽  
pp. 215-220
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Eiichi Bamba ◽  
Hiroshi Shibata
Keyword(s):  
Steady State ◽  
State Space ◽  
State Space Model ◽  
Model Identification ◽  
Output Data ◽  
Space Model ◽  
Input And Output

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.

A Dynamic State Space Model of a MHO Distance Relay

1985 ◽  
Vol PAS-104 (12) ◽  
pp. 3558-3564 ◽  
Author(s):  
Z. Peng ◽  
M. Li ◽  
C. Wu ◽  
T. Cheng ◽  
T. Ning
Keyword(s):  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Distance Relay ◽  
Space Model

A Dynamic State Space Model of an MHO Distance Relay

1985 ◽  
Vol PER-5 (12) ◽  
pp. 51-52
Author(s):  
Z. Peng ◽  
M. S. Li ◽  
C. Y. Wu ◽  
T. C. Cheng ◽  
T. S. Ning
Keyword(s):  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Distance Relay ◽  
Space Model
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