A brief introduction to the analysis and design of Networked Control Systems

Goodwin, Graham C.; Silva, E.I.; Quevedo, Daniel E.

doi:10.1109/ccdc.2008.4597262

Cited by 34 publications

(21 citation statements)

References 69 publications

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“…One problem with a long, slow decaying impulse response is that it affects the stability of the reconstruction, in the sense that bounded errors in the samples are able to produce unbounded point-wise error in the reconstructed output. As an example, consider the ideal low-pass reconstruction in (4). It is easy to show that any bounded periodical error in the samples a(kτ ) of the form ρ(−1) k k∈Z , with |ρ| > 0, will yield an unbounded reconstruction error in the L ∞ norm.…”

Section: Synthesis Filtermentioning

confidence: 99%

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Efficient Data Representations for Signal Processing and Control: "Making Most of a Little"

Goodwin

Derpich

Quevedo

2006

2006 Chinese Control Conference

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Abstract:In order that signals can be stored, transmitted or processed it is necessary that they first be converted into digital form. This, in turn, raises the problem of how to digitize data so as to achieve the best trade-off between data load and performance, i.e., "how to make the most out of a little". Two issues are involved in this problem, namely temporal quantization (i.e., sampling) and spatial quantization. These two problems have traditionally been addressed separately. Indeed, there exists substantial literature dealing with the temporal quantization problem, covering both band-limited and non-band-limited signals. The usual underlying paradigm is that of an analysis filter, followed by a sampler, followed by a reconstruction filter. Various parts of this architecture can be optimized once other parts have been specified. On the other hand, spatial quantization has been studied extensively for a given sampling strategy, particularly in the framework of sigma delta conversion. Finally, it is also possible to formulate the joint design problem for sampling and spatial quantization. This typically leads to enhanced performance compared to that achievable by considering the two aspects separately. This paper will survey the general area of sampling and quantization and analyze methods for achieving efficient data representations for signal processing and control applications. We will show how, on the one hand, contemporary control theory can contribute to the design of sampling and quantization systems and, on the other hand, how these systems impact on the performance of modern feedback control systems.

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Section: Synthesis Filtermentioning

confidence: 99%

“…• when signals need to be transmitted over a digital network, i.e., in Networked Control Systems (NCS's) [3,82,4];…”

Section: Applications To Controlmentioning

confidence: 99%

Efficient Data Representations for Signal Processing and Control: "Making Most of a Little"

Goodwin

Derpich

Quevedo

2006

2006 Chinese Control Conference

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“…Nair, Fagnani, Zampieri, and Evans 2007;Goodwin, Silva, and Quevedo 2008), one of the pivotal issues is 'how to quantise continuous-valued signals' to adapt them to command-driven devices. In particular, the design of dynamic quantisers, whose output depends on the current and past inputs, has received much attention due to the high performance beyond that of the static quantisers.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of optimally quantised LFT-feedback systems

Azuma

Sugie

2010

International Journal of Control

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This article is concerned with the stability analysis of quantised feedback control systems which contain the optimal dynamic quantisers recently proposed by the authors. First, it is shown that a sort of separation property of quantiser-controller design holds as far as the feedback system stability concerns. This allows us to design feedback controllers independently of the choice of quantisers. Then, based on this property, a necessary and sufficient condition is derived for the optimally quantised feedback system to be stable, which is described in terms of the poles/zeros of a linear feedback system to be quantised. Finally, we provide a class of suboptimal dynamic quantisers for which the resulting quantised control systems are guaranteed to be stable.

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“…Therefore, a lot of research efforts in the control community have lately been aimed at problems related to communication limitations. An overview of the research on networked control systems and control with data rate constraints, as well as a thorough list of references, can be found in [4] and [11] respectively.…”

Section: Introductionmentioning

confidence: 99%

Encoder and decoder design for signal estimation

Johannesson

Rantzer

Bernhardsson

et al. 2010

Proceedings of the 2010 American Control Conference

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Abstract-In this paper, we study the joint design of optimal linear encoders and decoders for filtering and transmission of a signal over an additive Gaussian noise channel subject to a real-time constraint. The objective is to minimize the variance of the estimation error at the receiving end. The design problem is nonconvex, but it is shown that a global optimum can be found by solving a related two-stage problem. The first stage consists of a mixed H 2 and H 1 norm minimization problem, where the H 2 norm corresponds to the error variance in a corresponding WienerKolmogorov filtering problem and the H 1 norm is induced by the channel noise. The second stage consists of a spectral factorization. The results are illustrated by a numerical example.

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A brief introduction to the analysis and design of Networked Control Systems

Cited by 34 publications

References 69 publications

Efficient Data Representations for Signal Processing and Control: "Making Most of a Little"

Efficient Data Representations for Signal Processing and Control: "Making Most of a Little"

Stability analysis of optimally quantised LFT-feedback systems

Encoder and decoder design for signal estimation

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