Iteratively reweighted correlation analysis method for robust parameter identification of multiple‐input multiple‐output discrete‐time systems

2016 ◽  
Vol 10 (5) ◽  
pp. 549-556 ◽  
Author(s):  
Zhu Wang ◽  
Qibing Jin ◽  
Xiaoping Liu
Keyword(s):  
Correlation Analysis ◽  
Discrete Time ◽  
Multiple Input Multiple Output ◽  
Analysis Method ◽  
Discrete Time Systems ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Correlation Analysis Method ◽  
Robust Parameter ◽  
Time Systems

scholarly journals A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
S. Alonso-Quesada ◽  
M. De la Sen ◽  
A. Ibeas
Keyword(s):  
Discrete Time ◽  
Iterative Learning Control ◽  
Linear Models ◽  
Multiple Input Multiple Output ◽  
Binary Sequence ◽  
Learning Control ◽  
Iterative Learning ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.

scholarly journals Complex-Coefficient Frequency Domain Stability Analysis Method for a Class of Cross-Coupled Antisymmetrical Systems and Its Extension in MSR Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yuan Ren ◽  
Jiancheng Fang
Keyword(s):  
Stability Analysis ◽  
Frequency Domain ◽  
Stability Criterion ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Complex Coefficient ◽  
Analysis Method ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Domain Stability

This paper develops a complex-coefficient frequency domain stability analysis method for a class of cross-coupled two-dimensional antisymmetrical systems, which can greatly simplify the stability analysis of the multiple-input multiple-output (MIMO) system. Through variable reconstruction, the multiple-input multiple-output (MIMO) system is converted into a single-input single-output (SISO) system with complex coefficients. The pole locations law of the closed-loop system after the variable reconstruction has been revealed, and the controllability as well as observability of the controlled plants before and after the variable reconstruction has been studied too, and then the classical Nyquist stability criterion is extended to the complex-coefficient frequency domain. Combined with the rigid magnetically suspended rotor (MSR) system with heavy gyroscopic effects, corresponding stability criterion has been further developed. Compared with the existing methods, the developed criterion for the rigid MSR system not only accurately predicts the absolute stability of the different whirling modes, but also directly demonstrates their relative stability, which greatly simplifies the analysis, design, and debugging of the control system.

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