Trade-off performance analysis of LTI system with channel energy constraint

Wang, Baoxian; Jiang, Xiao‐Wei; Chen, Chaoyang

doi:10.1016/j.isatra.2016.07.001

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…Qi et al [36] give a fundamental conditions of stabilizability. In recent years, we have also obtained some optimal performance results of NCSs, such as upstream and downstream channels with constraints [37], channel energy constaint [38], some novel trade-off factors and constraint channels [39], discrete-time(DT) systems with quantization [15], AWGN fading channels [40], SIMO systems with packetdropouts [41]. In spite of the significant progress on optimal performance studies, there are still many gaps in the optimal performance of NCSs.…”

Section: Introductionmentioning

confidence: 99%

Tracking Performance Limitations of MIMO Networked Control Systems with Multiple Communication Constraints

Chen,

Gui,

et al. 2019

Preprint

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In this paper, the tracking performance limitation of networked control systems (NCSs) is studied. The NCSs is considered as continuous-time linear multi-input multi-output (MIMO) systems with random reference noises. The controlled plants include unstable poles and non-minimum phase (NMP) zeros. The output feedback path is affected by multiple communication constraints. We focus on some basic communication constraints, including additive white noise (AWN), quantization noise, bandwidth, as well as encoder-decoder. The system performance is evaluated with the tracking error energy, and used a two-degree of freedom (2DOF) controller. The explicit representation of the tracking performance is given in this paper. The results indicate the tracking performance limitations rely to internal characteristics of the plant (unstable poles and NMP zeros), reference noises (the reference noise power distribution (RNPD) and its directions) and the characteristics of communication constraints. The characteristics of communication constraints include communication noise power distribution (CNPD), quantization noise power distribution (QNPD), and their distribution directions, transform bandwidth allocation (TBA), transform encoder-decoder allocation (TEA), and their allocation directions, and NMP zeros and MP part of bandwidth. Moreover, the tracking performance limitations are also affected by the angles between the each transform NMP zero direction and RNPD direction, and these angles between each transform unstable poles direction and the direction of communication constraint distribution/allocation. In addition, for MIMO NCSs, bandwidth (there are not identical two channels) always can affects the direction of unstable poles, and the channel allocation of bandwidth and encode-decode may be used for a feasible method for the performance allocation of each channels. Lastly, a instance is given for verifying the effectiveness of the theoretical outcomes.

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Section: Introductionmentioning

confidence: 99%

Tracking Performance Limitations of MIMO Networked Control Systems with Multiple Communication Constraints

Chen,

Gui,

et al. 2019

Preprint

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“…In recent years, more and more researchers are interested in networked control systems (NCSs), for example [2,3,10,15,20,21,24,25,27], and references therein. The most problems under consideration focus on how to model the networked control system and stabilization analysis with quantization effects [9,16,22], time delays [9,13,14,28], bandwidth constraint [5,6,19], and signal-to-noise ratio (SNR) constraint [4,7,17,18] over the communication channels.…”

Section: Introductionmentioning

confidence: 99%

Optimal tracking performance of discrete-time systems with quantization

Chen¹,

Gui²,

Zhou³

et al. 2017

J. Nonlinear Sci. Appl.

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This paper studies optimal tracking performance issues for linear time invariant system with two-channel constraints. The specific problem under consideration is quantization for up-link and down-link communication channel which satisfies some constraints. Logarithmic quantization law is employed in the quantizers. The tracking performance is defined in an square sense, and the reference signal under consideration in this paper is a step signal. The system's reference signal is considered as a step signal. The tracking performance is measured by the minimum mean square error between the reference input and the system's output. By using dynamic programming approach, discrete-time algebraic Riccati equation (ARE) is obtained. The optimal tracking performance is obtained by output feedback control, in terms of the space equation of the given system and the unique solution of the discrete-time algebraic Riccati equation. And, the impact of quantizer for optimal tracking performance is analyzed. Finally, simulation example is given to illustrate the theoretical results.

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