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H∞ fault estimation with randomly occurring uncertainties, quantization effects and successive packet dropouts: The finite-horizon case
In this paper, the finite-horizon H∞ fault estimation problem is investigated for a class of uncertain nonlinear time-varying systems subject to multiple stochastic delays. The randomly occurring uncertainties (ROUs) enter into the system due to the random fluctuations of network conditions. The measured output is quantized by a logarithmic quantizer before being transmitted to the fault estimator. Also, successive packet dropouts (SPDs) happen when the quantized signals are transmitted through an unreliable network medium. Three mutually independent sets of Bernoulli-distributed white sequences are introduced to govern the multiple stochastic delays, ROUs and SPDs. By employing the stochastic analysis approach, some sufficient conditions are established for the desired finite-horizon fault estimator to achieve the specified H∞ performance. The time-varying parameters of the fault estimator are obtained by solving a set of recursive linear matrix inequalities. Finally, an illustrative numerical example is provided to show the effectiveness of the proposed fault estimation approach
Stability of quantized time-delay nonlinear systems: A Lyapunov-Krasowskii-functional approach
Lyapunov-Krasowskii functionals are used to design quantized control laws for
nonlinear continuous-time systems in the presence of constant delays in the
input. The quantized control law is implemented via hysteresis to prevent
chattering. Under appropriate conditions, our analysis applies to stabilizable
nonlinear systems for any value of the quantization density. The resulting
quantized feedback is parametrized with respect to the quantization density.
Moreover, the maximal allowable delay tolerated by the system is characterized
as a function of the quantization density.Comment: 31 pages, 3 figures, to appear in Mathematics of Control, Signals,
and System
Data-Driven Superstabilization of Linear Systems under Quantization
This paper focuses on the stabilization and regulation of linear systems
affected by quantization in state-transition data and actuated input. The
observed data are composed of tuples of current state, input, and the next
state's interval ranges based on sensor quantization. Using an established
characterization of input-logarithmically-quantized stabilization based on
robustness to sector-bounded uncertainty, we formulate a nonconservative
infinite-dimensional linear program that enforces superstabilization of all
possible consistent systems under assumed priors. We solve this problem by
posing a pair of exponentially-scaling linear programs, and demonstrate the
success of our method on example quantized systems.Comment: 12 pages, 2 figures, 3 table
Time-and event-driven communication process for networked control systems: A survey
Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Self-triggered Stabilization of Contracting Systems under Quantization
We propose self-triggered control schemes for nonlinear systems with
quantized state measurements. Our focus lies on scenarios where both the
controller and the self-triggering mechanism receive only the quantized state
measurement at each sampling time. We assume that the ideal closed-loop system
without quantization or self-triggered sampling is contracting. Moreover, a
growth rate of the open-loop system is assumed to be known. We present two
control strategies that yield the closed-loop stability without Zeno behavior.
The first strategy is implemented under logarithmic quantization and imposes no
time-triggering condition other than setting an upper bound on inter-sampling
times. The second one is a joint design of zooming quantization and periodic
self-triggered sampling, where the adjustable zoom parameter for quantization
changes based on inter-sampling times and is also used for the threshold of
self-triggered sampling. In both strategies, we employ a trajectory-based
approach for stability analysis, where contraction theory plays a key role.Comment: 26 pages, 10 figure
Quantization effects and convergence properties of rigid formation control systems with quantized distance measurements
In this paper, we discuss quantization effects in rigid formation control
systems when target formations are described by inter-agent distances. Because
of practical sensing and measurement constraints, we consider in this paper
distance measurements in their quantized forms. We show that under
gradient-based formation control, in the case of uniform quantization, the
distance errors converge locally to a bounded set whose size depends on the
quantization error, while in the case of logarithmic quantization, all distance
errors converge locally to zero. A special quantizer involving the signum
function is then considered with which all agents can only measure coarse
distances in terms of binary information. In this case, the formation converges
locally to a target formation within a finite time. Lastly, we discuss the
effect of asymmetric uniform quantization on rigid formation control.Comment: 29 pages, International Journal of Robust and Nonlinear Control 201
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