Parsimonious Bayesian Filtering in Markov Jump Systems With Applications to Networked Control

Static Output Feedback Quantized Control for Fuzzy Markovian Switching Singularly Perturbed Systems With Deception Attacks

“…} and postmultiplying its transpose to (39), one derives that (39) guarantees ( 16) and (17). The proof is complete.…”

“…Since the concept of Markov switching systems (MSSs) was proposed by Krasovskii and Lidskii [12], many physical systems whose structures or parameters are undergoing with random abrupt changes can be modeled effectively, and the growing attention of scholars in many research fields have been drawn. Because of the advantage in modeling comprehensive dynamic systems, many issues have been addressed for MSSs, including stabilization [13]- [15], estimation [16], filtering [17], robust control [18], [19], and sliding-mode control [20]. When Markov switching parameters are involved in SPSs, it can be described by Markovian switching singularly perturbed systems (MSSPSs).…”

Event‐triggered asynchronous filtering for networked fuzzy non‐homogeneous Markov jump systems with dynamic quantization

“…1,4,5 In MJSs, these abrupt changes are modelled by a Markov chain governed switch rule among the "modes", where the switch rule is usually described by a mode transition probability matrix (MTPM), and a mode is referring to a subsystem of the MJS whose dynamics are relatively stable. For homogeneous MJSs, that is, MJSs with time-invariant MTPM, existing achievements can refer to Costa et al, 1 Mesquita, 6 Geromel et al 7 and Zhu et al 8 for the stability and stabilization, filtering, minimax control and mode feedback control of MJSs. On the other hand, non-homogeneous MJSs, that is, MJSs with time-varying MTPM, are more general and challenging.…”