A systematic method to obtain ultimate bounds for perturbed systems

Kofman, Ernesto; Haimovich, Hernan; Serón, María M.

doi:10.1080/00207170600611265

Cited by 154 publications

(195 citation statements)

References 13 publications

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“…In this paper, the notion of RPI sets and the method to construct those are based on the results of Kofman et al (2007), Kolmanovsky and Gilbert (1998), and Olaru et al (2010), which are given in Appendix.…”

Section: Fault Detection and Isolationmentioning

confidence: 99%

Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation

Puig

Ocampo‐Martínez

et al. 2014

53rd IEEE Conference on Decision and Control

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In this paper, a fault-tolerant control (FTC) scheme is proposed for actuator faults, which is built upon tube-based model predictive control (MPC) as well as set-based fault detection and isolation (FDI). In the class of MPC techniques, tubebased MPC can effectively deal with system constraints and uncertainties with relatively low computational complexity compared with other robust MPC techniques such as min-max MPC. Set-based FDI, generally considering the worst case of uncertainties, can robustly detect and isolate actuator faults. In the proposed FTC scheme, fault detection (FD) is passive by using invariant sets, while fault isolation (FI) is active by means of MPC and tubes. The active FI method proposed in this paper is implemented by making use of the constraint-handling ability of MPC to manipulate the bounds of inputs. After the system has been detected to become faulty, the input-constraint set of the MPC controller is adjusted to actively excite the system for achieving FI guarantees on-line, where an active FI-oriented input set is designed off-line. In this way, the system can be excited in order to obtain more extra system-operating information for FI than passive FI approaches. Overall, the objective of this paper is to propose an actuator MPC scheme with as little as possible of FI conservatism and computational complexity by combining tube-based MPC and set theory within the framework of MPC, respectively. Finally, a case study is used to show the effectiveness of the proposed FTC scheme.

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Section: Fault Detection and Isolationmentioning

confidence: 99%

Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation

Puig

Ocampo‐Martínez

et al. 2014

53rd IEEE Conference on Decision and Control

View full text Add to dashboard Cite

show abstract

“…These works presented a new framework to obtain closed-form ultimate bound formulae based on the use of componentwise perturbation bounds and componentwise analysis of the system. As compared with the bounds obtained by means of the standard quadratic-Lyapunov-function approach, the examples in Kofman (2005) and Kofman et al (2007) show that this componentwise framework may in some cases provide much tighter bounds. This componentwise framework has been developed for purely continuous-and discrete-time systems and cannot be directly applied to sampled-data systems.…”

Section: Introductionmentioning

confidence: 99%

“…A method to estimate ultimate bounds that does not require the selection of a Lyapunov function, nor bounding the norm of the perturbation, was introduced in Kofman (2005) for continuous-time linear time-invariant (LTI) systems and extended in Kofman et al (2007). These works presented a new framework to obtain closed-form ultimate bound formulae based on the use of componentwise perturbation bounds and componentwise analysis of the system.…”

Section: Introductionmentioning

confidence: 99%

“…We follow ideas similar to those used in Kofman (2005) and Kofman et al (2007), and derive new results that can be directly applied to perturbed sampleddata systems to obtain global ultimate bounds. These bounds take intersample behavior into account and can be calculated in a systematic way, i.e., they do not require adjustment of parameters or selection of suitable norms or Lyapunov functions.…”

Section: Introductionmentioning

confidence: 99%

“…This componentwise framework has been developed for purely continuous-and discrete-time systems and cannot be directly applied to sampled-data systems. Moreover, when the perturbation bound depends on the system state, Kofman et al (2007) provides ultimate bounds that are only locally valid.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Systematic ultimate bound computation for sampled-data systems with quantization

2007

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We present a novel systematic method to obtain componentwise ultimate bounds in perturbed sampled-data systems, especially when the perturbations arise due to quantization. The proposed method exploits the system geometry as well as the perturbation structure, and takes intersample behavior into account. The main features of the method are its systematic nature, whereby it can be readily computer coded, without requiring adjustment of parameters for its application, and its suitability for dealing with highly structured perturbation schemes, whereby the information on the perturbation structure is directly taken into account. The latter feature distinguishes the method from other approaches that require a bound on the norm of the perturbation and thus disregard information on the perturbation structure. We apply the method to a numerical example taken from the literature to illustrate its simplicity and potential.

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Bibliography

2013

Set‐theoretic Fault‐tolerant Control in Multisensor Systems

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A systematic method to obtain ultimate bounds for perturbed systems

Cited by 154 publications

References 13 publications

Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation

Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation

Systematic ultimate bound computation for sampled-data systems with quantization

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