Gain Scheduled Fault Detection Filter for Markovian Jump Linear System with Nonhomogeneous Markov Chain

In a networked control system scenario, the packet dropout is usually modeled by a time-invariant (homogeneous) Markov chain (MC) process. However, from a practical point of view, the probabilities of packet loss can vary in time and/or probability parameter dependency. Therefore, to design a fault...

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Published inMathematics (Basel) Vol. 11; no. 7; p. 1713
Main Authors Carvalho, Leonardo, Palma, Jonathan M., Morais, Cecília F., Jayawardhana, Bayu, Costa, Oswaldo L. V.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2023
Subjects
<named-content content-type="inline-formula"><mml:math id="mm1111"><mml:mi mathvariant="script">H</mml:mi>∞</mml:math></named-content> norm
Design parameters
Fault detection
Fault location (Engineering)
fault-detection filter
H-infinity control
Linear systems
LMI relaxations
Markov analysis
Markov chains
Markov processes
Markovian jump linear system
Mathematical models
Methods
Network control
nonhomogeneous Markov chains
Numerical analysis
Probability
Simulation
Systems stability
Brazil
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Abstract In a networked control system scenario, the packet dropout is usually modeled by a time-invariant (homogeneous) Markov chain (MC) process. However, from a practical point of view, the probabilities of packet loss can vary in time and/or probability parameter dependency. Therefore, to design a fault detection filter (FDF) implemented in a semi-reliable communication network, it is important to consider the variation in time of the network parameters, by assuming the more accurate scenario provided by a nonhomogeneous jump system. Such a premise can be properly taken into account within the linear parameter varying (LPV) framework. In this sense, this paper proposes a new design method of H∞ gain-scheduled FDF for Markov jump linear systems under the assumption of a nonhomogeneous MC. To illustrate the applicability of the theoretical solution, a numerical simulation is presented.
AbstractList In a networked control system scenario, the packet dropout is usually modeled by a time-invariant (homogeneous) Markov chain (MC) process. However, from a practical point of view, the probabilities of packet loss can vary in time and/or probability parameter dependency. Therefore, to design a fault detection filter (FDF) implemented in a semi-reliable communication network, it is important to consider the variation in time of the network parameters, by assuming the more accurate scenario provided by a nonhomogeneous jump system. Such a premise can be properly taken into account within the linear parameter varying (LPV) framework. In this sense, this paper proposes a new design method of H∞ gain-scheduled FDF for Markov jump linear systems under the assumption of a nonhomogeneous MC. To illustrate the applicability of the theoretical solution, a numerical simulation is presented.
In a networked control system scenario, the packet dropout is usually modeled by a time-invariant (homogeneous) Markov chain (MC) process. However, from a practical point of view, the probabilities of packet loss can vary in time and/or probability parameter dependency. Therefore, to design a fault detection filter (FDF) implemented in a semi-reliable communication network, it is important to consider the variation in time of the network parameters, by assuming the more accurate scenario provided by a nonhomogeneous jump system. Such a premise can be properly taken into account within the linear parameter varying (LPV) framework. In this sense, this paper proposes a new design method of H ∞ gain-scheduled FDF for Markov jump linear systems under the assumption of a nonhomogeneous MC. To illustrate the applicability of the theoretical solution, a numerical simulation is presented.
In a networked control system scenario, the packet dropout is usually modeled by a time-invariant (homogeneous) Markov chain (MC) process. However, from a practical point of view, the probabilities of packet loss can vary in time and/or probability parameter dependency. Therefore, to design a fault detection filter (FDF) implemented in a semi-reliable communication network, it is important to consider the variation in time of the network parameters, by assuming the more accurate scenario provided by a nonhomogeneous jump system. Such a premise can be properly taken into account within the linear parameter varying (LPV) framework. In this sense, this paper proposes a new design method of H[sub.∞] gain-scheduled FDF for Markov jump linear systems under the assumption of a nonhomogeneous MC. To illustrate the applicability of the theoretical solution, a numerical simulation is presented.
Audience Academic
Author Jayawardhana, Bayu
Costa, Oswaldo L. V.
Palma, Jonathan M.
Morais, Cecília F.
Carvalho, Leonardo
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Design parameters
Fault detection
Fault location (Engineering)
fault-detection filter
H-infinity control
Linear systems
LMI relaxations
Markov analysis
Markov chains
Markov processes
Markovian jump linear system
Mathematical models
Methods
Network control
nonhomogeneous Markov chains
Numerical analysis
Probability
Simulation
Systems stability
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Title Gain Scheduled Fault Detection Filter for Markovian Jump Linear System with Nonhomogeneous Markov Chain
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