Low-Order Stabilizer Design for Discrete Linear Time-Varying Internal Model-Based System

2015 ◽  
Vol 20 (6) ◽  
pp. 2666-2677 ◽  
Author(s):  
Xingyong Song ◽  
Pradeep Kumar Gillella ◽  
Zongxuan Sun
Keyword(s):  
Internal Model ◽  
Linear Time ◽  
Time Varying ◽  
Model Based ◽  
Low Order

An Internal Model-Based Tracking Control for a Class of Uncertain Linear Time-Varying Systems

2010 ◽  
Author(s):  
Zhen Zhang ◽  
Zongxuan Sun ◽  
Peiqing Ye
Keyword(s):  
Tracking Control ◽  
Internal Model ◽  
Linear Time ◽  
Design Method ◽  
Uncertain System ◽  
Feedback Gain ◽  
Time Varying ◽  
Linear Parameter Varying ◽  
Model Based ◽  
Time Varying Systems

In this paper, we extend previous results for a novel internal model-based tracking control with a class of known LTV plant models driven by LTI exosystems to uncertain LTI plant models driven by LTV exosystems. The augmented time-varying system to be stabilized becomes uncertain. Moreover, the time-varying fashion under consideration renders the augmented uncertain system linear parameter-varying (LPV). By means of an output-feedback gain-scheduling design, the augmented uncertain LTV system is stabilized. Simulation results illustrate the proposed design method.

A Novel Internal Model-Based Tracking Control for a Class of Linear Time-Varying Systems

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
Zhen Zhang ◽  
Zongxuan Sun
Keyword(s):  
Internal Model ◽  
Linear Time ◽  
Implementation Process ◽  
Automatic Generation ◽  
Feedback Mechanism ◽  
Control Input ◽  
Time Varying ◽  
Linear Time Invariant ◽  
Model Based ◽  
Time Varying Systems

This paper provides a novel method of constructing an internal model-based design of reference tracking and/or disturbance rejection for a class of linear time-varying plants with a known linear time invariant (LTI) exosystem. It is shown how the realization of an appropriate time-varying internal model can be constructed by means of a novel feedback mechanism. The design of the internal model consists of two ingredients: (1) a time-varying system immersion of the exosystem, and (2) an automatic generation of the desired control input to render the error-zeroing subspace invariant, based on the complete knowledge of the plant model. The important features of the proposed method lie in that the tracking problem setup and the proposed feedback mechanism allow us to avoid explicitly calculating the desired input, which keeps the regulated error identically at zero. Moreover the time-varying immersion is guaranteed to hold for the class of plant models under consideration. These features significantly broaden the range of applications of the proposed method, and simplify the control implementation process.

scholarly journals A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

2019 ◽  
Vol 11 (14) ◽  
pp. 3832 ◽  
Author(s):  
Pingping Xiong ◽  
Jia Shi ◽  
Lingling Pei ◽  
Song Ding
Keyword(s):  
Linear Time ◽  
Dynamic Change ◽  
Time Varying ◽  
The Novel ◽  
Model Based ◽  
Number Sequences ◽  
The Government ◽  
Grey Number ◽  
Beijing China

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM10, SO2 and NO2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

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