Closed-Loop Subspace Identification Algorithm of EIV Model Based on Orthogonal Decomposition and PCA

2012 ◽  
pp. 55-65
Author(s):  
Jianguo Wang ◽  
Yong Guo ◽  
Juanjuan Wang
Keyword(s):  
Closed Loop ◽  
Orthogonal Decomposition ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Model Based

scholarly journals Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaosuo Luo ◽  
Yongduan Song
Keyword(s):  
Predictive Control ◽  
Closed Loop ◽  
Control Method ◽  
Negative Impact ◽  
Data Driven ◽  
Recursive Identification ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Closed Loop Identification ◽  
Adaptive Predictive Control

This paper presents a data-driven adaptive predictive control method using closed-loop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace identification algorithm driven by input-output data. Taking advantage of transformational system model, the closed-loop data is effectively processed in this subspace algorithm. By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given. Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance. The problems of online excitation data inaccuracy and closed-loop identification in adaptive control are well solved in the proposed method. Simulation results show the efficiency of this method.

scholarly journals Multivariable Constrained Adaptive Predictive Control based on Closed-loop Subspace Identification

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Predictive Control ◽  
Closed Loop ◽  
Control Method ◽  
Open Loop ◽  
System Model ◽  
Quadratic Program ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Prediction Errors ◽  
Adaptive Predictive Control

In order to deal with nonlinear, time-varying, and multivariable constrained characteristics in closed-loop industrial processes, a multivariable constrained adaptive predictive control (CAPC) method based on closed-loop subspace identification is proposed. The state-space model is obtained through the closed-loop subspace identification algorithm, which is regarded as the system model. The algorithm is implemented online to update the R matrix with a receding window. By comparing the prediction errors before and after updating, it considers whether or not to update the system model. The model is then used to design the model predictive controller, which involves the solution of a quadratic program solving multivariable constraints. This paper presents a comparison between the performance of the proposed control method when applied to a 2-CSTR system, and that of an open-loop subspace CAPC method. The superiority of the proposed method is illustrated by the simulation results.

scholarly journals Hankel Matrix Correlation Function-Based Subspace Identification Method for UAV Servo System

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Minghong She ◽  
Pengju Zhao
Keyword(s):  
Correlation Function ◽  
Closed Loop ◽  
Hankel Matrix ◽  
Servo System ◽  
Identification Problem ◽  
Open Loop ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Related Data ◽  
Multiplication Operation

For the identification problem of closed-loop subspace model, we propose a zero space projection method based on the estimation of correlation function to fill the block Hankel matrix of identification model by combining the linear algebra with geometry. By using the same projection of related data in time offset set and LQ decomposition, the multiplication operation of projection is achieved and dynamics estimation of the unknown equipment system model is obtained. Consequently, we have solved the problem of biased estimation caused when the open-loop subspace identification algorithm is applied to the closed-loop identification. A simulation example is given to show the effectiveness of the proposed approach. In final, the practicability of the identification algorithm is verified by hardware test of UAV servo system in real environment.

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