Optimal Tracking Performance With Model Uncertainty Over A Quantized Control Input
作者单位：Department of Control Science and Engineering Huazhong University of Science and Technology School of Mathematics and Statistics Anyang Normal University College of Mechatronics and Control Engineering Hubei Normal University
会议名称：《第25届中国控制与决策会议》
主办单位：IEEE;NE Univ;IEEE Ind Elect Chapter;IEEE Harbin Sect Control Syst Soc Chapter;Guizhou Univ;IEEE Control Syst Soc;Syst Engn Soc China;Chinese Assoc Artificial Intelligence;Chinese Assoc Automat;Tech Comm Control Theory;Chinese Assoc Aeronaut;Automat Control Soc;Chinese Assoc Syst Simulat;Simulat Methods & Modeling Soc;Intelligent Control & Management Soc
会议日期：2013年
学科分类：0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0701[理学-数学]
基 金：partially supported by the National Nature Science Foundation of China under Grants 61073065, 61073026, 61100076 and 61272069 the 973 National Basic Research Program of China under Grant 2011CB013300 and 2011CB013301
关 键 词：quantization tracking performance limitation networked control systems uncertainty non-minimum phase zeros.
摘 要：There has recently been significant interest in feedback control problems with communication constraints. But the existed work mainly assume that the plant has an exact model. The object of this paper is to investigate the optimal tracking problems for networked control system in the presence of plant uncertainty. This paper studies optimal tracking performance issues for finite-dimensional, continuous-time, linear time-invariant （LTI） networked control systems, in which the control input is subject to a quantization and the plant model with uncertainty. The problem under consideration amounts to determining the minimal tracking error, while we consider two-parameter controllers. The plant is assumed to be non-minimum phase and unstable, that is, the plant contains zeros in the right half and unstable poles. And we formulate the uncertainty by utilizing stochastic embedding. The integral square criterion is adopted to measure the tracking error. The results show that the quantization error and the model uncertainty, as well as the non-minimum phase zeros, can worsen the tracking performance.