Reliability-based measures of structural control robustness

Spencer, Billie F.; Sain, M.K.; Won, Chang-Hee; Kaspari, D. C.; Sain, P.M.

doi:10.1016/0167-4730(94)90055-8

Cited by 81 publications

(40 citation statements)

References 13 publications

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“…In general, these time-varying control objectives depending on the system state or trajectory are not applicable to μ(·), except for those that can be directly calculated based on a system model, such as the guaranteed cost control (Polyak and Tempo, 2001). If the timevarying control objectives are to maintain the system trajectory within a safety region under a Gaussian noise disturbance, the methods presented in Spencer et al (1994) can be used instead to estimate the probabilistic performance for reliability evaluation. The performance value…”

Section: Assumptions the Assumptions Made In This Paper Are As Follomentioning

confidence: 99%

“…Moreover, this analysis is a prerequisite to reliability-based controller design. For example, in the reliability-based design of structural control, the key problem is to evaluate the failure probability, a complementary reliability index (Spencer et al, 1994). For Fault Tolerant Control (FTC), improving the system reliability is considered to be the ultimate goal.…”

Section: Introductionmentioning

confidence: 99%

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Reliability Modeling of Fault Tolerant Control Systems

Li¹,

Zhao²,

Yang³

2007

International Journal of Applied Mathematics and Computer Science

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This paper proposes a novel approach to reliability evaluation for active Fault Tolerant Control Systems (FTCSs). By introducing a reliability index based on the control performance and hard deadline, a semi-Markov process model is proposed to describe system operation for reliability evaluation. The degraded performance of FTCSs in the presence of imperfect Fault Detection and Isolation (FDI) is reflected by semi-Markov states. The semi-Markov kernel, the key parameter of the process, is determined by four probabilistic parameters based on the Markovian model of FTCSs. Computed from the transition probabilities of the semi-Markov process, the reliability index incorporates control objectives, hard deadline, and the effects of imperfect FDI, a suitable quantitative measure of the overall performance.

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Section: Assumptions the Assumptions Made In This Paper Are As Follomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability Modeling of Fault Tolerant Control Systems

Li¹,

Zhao²,

Yang³

2007

International Journal of Applied Mathematics and Computer Science

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“…h max = h 2 and h min = h 1 in Equation (10). Reliability metrics for the e random variables x(h i ), are estimated via the hybrid approach and then used to form the integrands in Equations (10)(11).…”

Section: Hybrid Approachmentioning

confidence: 99%

“…On the other hand, asymptotic approximations [10][11][12] for the estimation of failure probabilities have been only used as a control analysis tool. This paper integrates these numerical tools.…”

Section: Introductionmentioning

confidence: 99%

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Reliability-Based Control Design for Uncertain Systems

Crespo

Kenny

2005

Journal of Guidance, Control, and Dynamics

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This paper presents a robust control design methodology for systems with probabilistic parametric uncertainty. Control design is carried out by solving a reliability-based multi-objective optimization problem where the probability of violating design requirements is minimized. Simultaneously, failure domains are optimally enlarged to enable global improvements in the closed-loop performance. To enable an efficient numerical implementation, a hybrid approach for estimating reliability metrics is developed. This approach, which integrates deterministic sampling and asymptotic approximations, greatly reduces the numerical burden associated with complex probabilistic computations without compromising the accuracy of the results. Examples using output-feedback and full-state feedback with state estimation are used to demonstrate the ideas proposed.

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Reliability‐based robust control for uncertain dynamical systems using feedback of incomplete noisy response measurements

Yuen

Beck

2003

Earthq Engng Struct Dyn

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SUMMARYA reliability-based output feedback control methodology is presented for controlling the dynamic response of systems that are represented by linear state-space models. The design criterion is based on a robust failure probability for the system. This criterion provides robustness for the controlled system by considering a probability distribution over a set of possible system models with a stochastic model of the excitation so that robust performance is expected. The control command signal can be calculated using incomplete response measurements at previous time steps without requiring state estimation. Examples of robust structural control using an active mass driver on a shear building model and on a benchmark structure are presented to illustrate the proposed method.

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Reliability-based measures of structural control robustness

Cited by 81 publications

References 13 publications

Reliability Modeling of Fault Tolerant Control Systems

Reliability Modeling of Fault Tolerant Control Systems

Reliability-Based Control Design for Uncertain Systems

Reliability‐based robust control for uncertain dynamical systems using feedback of incomplete noisy response measurements

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