Stochastic stability analysis for continuous-time fault tolerant control systems

Srichander, R.; Walker, Bruce K.

doi:10.1080/00207179308934397

Cited by 140 publications

(13 citation statements)

References 20 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The weak infinitesimal operator A is defined as the derivative of V(z(t),i,t) along the trajectory of the joint Markov process z(t), i, t ≥ 0 at the point z(t), i and t [18].…”

Section: Construct a Stochastic Lyapunov Function Asmentioning

confidence: 99%

Dynamic output feedback control of uncertain networked control systems

Deng

Fei

2009

Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

The paper focuses on the problem of output feedback control for uncertain networked control systems(NCSs) that possess random time-delay which is described by a Markov process. Based on Lyapunov-Razumikhin method a dynamic output feedback controller is proposed to stabilize the class of NCSs. A sufficient condition for existence of such controller is given in terms of bilinear matrix inequalities (BMIs). A modified algorithm is used to solve the BMIs. A numerical example illustrates the utility of the proposed approach.

show abstract

“…The weak infinitesimal operator A is defined as the derivative of V(z(t),i,t) along the trajectory of the joint Markov process z(t), i, t ≥ 0 at the point z(t), i and t [18].…”

Section: Construct a Stochastic Lyapunov Function Asmentioning

confidence: 99%

Dynamic output feedback control of uncertain networked control systems

Deng

Fei

2009

Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…The weak infinitesimal operatorÃ can be considered as the derivative of the function of V (x(t), η 1 (t), η 2 (t), t) along the trajectory of the joint Markov process {x(t), η 1 (t), η 2 (t), t ≥ 0} at the point {x(t), η 1 (t) = i, η 2 (t) = k} at time t; see [7] and [11].Ã…”

Section: T)+ρ(kt))mentioning

confidence: 99%

State feedback control of uncertain networked control systems with random time-delays

Huang

Nguang

2007

2007 46th IEEE Conference on Decision and Control

View full text Add to dashboard Cite

This paper investigates the stabilisation problem for a class of linear uncertain networked control systems (NCSs) with random communication time-delays. Both sensor-tocontroller and controller-to-actuator random network-induced delays are considered. Markov processes are used to model these random network-induced delays. Based on the Lyapunov-Razumikhin method a mode-dependent state feedback controller is proposed to stabilize this class of systems. The existence of such controller is given in terms of the solvability of bilinear matrix inequalities (BMIs), which are to be solved by a newly proposed algorithm. A numerical example is used to illustrate the validity of the design methodology.

show abstract

“…However, the results are based on the assumption of certainty o f correct fault identi cation. To relax this assumption for better description of AFTCS, another class of hybrid systems was de ned in 168 . This class of systems is known as Fault Tolerant C o n trol Systems with Markovian Parameters FTCSMP.…”

Section: Advances In Fault Tolerant Control Systemsmentioning

confidence: 99%

Active Fault Tolerant Control Systems

Mahmoud¹,

Jang²,

Zhang³

2003

Lecture Notes in Control and Information Sciences

174

View full text Add to dashboard Cite

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Digital data supplied by author. Data-conversion by PTP-Berlin, Stefan Sossna e.K. Cover-Design: design & production GmbH, Heidelberg Printed on acid-free paper 62/3020Rw -5 4 3 2 1 0 TO OUR WIVES, BELOVED CHILDREN AND DEAR PARENTS Preface Modern technological systems rely on sophisticated control functions to meet increased performance requirements. For such systems, Fault Tolerant Control Systems FTCSs need to bedeveloped. FTCSs can bebroadly classi ed into passive and active.A Passive FTCS PFTCS can tolerate a prede ned set of faults while accomplishing its mission satisfactory without the need for control recon guration.Active FTCS AFTCS, on the other hand, relies on a Fault Detection and Identication FDI process to monitor system performance, and to detect and isolate faults in the system. Accordingly, the control law is recon gured on-line. The dynamic behavior of AFTCS can be modelled by S t o c hastic Di erential Equations SDE, due to the fact that faults are random in nature, and the FDI decisions are non-deterministic.In general, SDE can beclassi ed into two categories: SDE perturbed by white Gaussian noise Ito di erential equations, and SDE whose coe cients vary randomly with Markovian characteristics hybrid systems.The dynamic behavior of an AFTCS belongs to the class of hybrid systems. It is hybrid because it combines boththe Euclidean space for system dynamics and the discrete space for fault-induced changes. Stochastic stability of AFTCS is of prime importance. Substantial results for the stability of hybrid systems were obtained using the Lyapunov function approach and the supermartingale property.

show abstract

Stochastic stability analysis for continuous-time fault tolerant control systems

Cited by 140 publications

References 20 publications

Dynamic output feedback control of uncertain networked control systems

Dynamic output feedback control of uncertain networked control systems

State feedback control of uncertain networked control systems with random time-delays

Active Fault Tolerant Control Systems

Contact Info

Product

Resources

About