Analysis and Design of Markov Jump Systems with Complex Transition Probabilities

Zhang, Lixian; Yang, Ting; Shi, Peng; Zhu, Yanzheng

doi:10.1007/978-3-319-28847-5

Cited by 46 publications

(20 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…wherẽ= −̂represents the estimation error of , and is a positive design parameter. By (8) and Young's inequality, the infinitesimal generator of V 1 along system (12) is given by…”

Section: Adaptive Tracking Controller Designmentioning

confidence: 99%

Adaptive tracking control for a class of random pure‐feedback nonlinear systems with Markovian switching

Yao

Zhang

2018

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This note studies the tracking control problem for a class of random pure-feedback nonlinear systems with Markovian switching and unknown parameters. An adaptive tracking controller is constructed by introducing an auxiliary integrator subsystem and using the improved backstepping method such that the closed-loop system has a unique solution that is globally bounded in probability. Meanwhile, the tracking error can converge to an arbitrarily small neighborhood of zero via the parameter regulation technique. The efficiency of the tracking controller designed in this paper is demonstrated by simulation examples. KEYWORDSbackstepping, Markovian switching, random pure-feedback systems, tracking control 3112

show abstract

“…wherẽ= −̂represents the estimation error of , and is a positive design parameter. By (8) and Young's inequality, the infinitesimal generator of V 1 along system (12) is given by…”

Section: Adaptive Tracking Controller Designmentioning

confidence: 99%

Adaptive tracking control for a class of random pure‐feedback nonlinear systems with Markovian switching

Yao

Zhang

2018

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…where By using the well-known Schur complement lemma, 21 performing a congruence transformation to Λ(i, l) by diag(G, I 2Nn x ×2Nn x , I 2Nn x ×2Nn x ) and noting the fact that −2G + P ≥ −GP −1G , it can be derived that…”

Section: Theorem 1 the Mjds (6) Without Exogenous Disturbance Is Stomentioning

confidence: 99%

“…It is worth mentioning an additional number of network‐induced imperfections such as random time delays, random data packet dropouts, stochastic switching topologies subject to working environment (normal or failure) and the mobility of the sensor node, size changing, unreliable communication links, randomly occurring sensor saturations and missing measurements, and randomly occurring uncertainties and nonlinearities . For these random imperfections, Markov process/chain has been proven as a powerful tool for modeling these phenomena.…”

Section: Introductionmentioning

confidence: 99%

Collaborative distributed design for wireless control systems with Markovian‐type control network and distributed network‐induced time delays

Wen

Nguang

Zhao

2018

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary In this paper, collaborative distributed dynamic output‐feedback control problem is investigated for a class of wireless control systems with Markovian‐type control network and distributed network‐induced delays. The network consists of a set of interconnected nodes characterized by a directed graph with an adjacency matrix that specifies space distribution (ie, interconnection topology). Each node of wireless control systems not only routes information but also performs its local control. We assume measurements and distributed network‐induced delays are subject to two independent Markovian chains. It is shown that the collaborative distributed dynamic output‐feedback approach offers better performances compared with noncollaborative distributed control approach. An irrigation canal system is used to demonstrate practical potentials of the developed control strategy.

show abstract

“…Markov jump system models have attracted more and more attention because they effectively described this kind of systems and have become a hot topic in control theory. Many mature and systematic results have been obtained [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis for a Class of Discrete‐Time Nonhomogeneous Markov Jump Systems with Multiplicative Noises

Zhou

Chen

et al. 2018

Complexity

View full text Add to dashboard Cite

This paper is concerned with a class of discrete-time nonhomogeneous Markov jump systems with multiplicative noises and timevarying transition probability matrices which are valued on a convex polytope. The stochastic stability and finite-time stability are considered. Some stability criteria including infinite matrix inequalities are obtained by parameter-dependent Lyapunov function. Furthermore, infinite matrix inequalities are converted into finite linear matrix inequalities (LMIs) via a set of slack matrices. Finally, two numerical examples are given to demonstrate the validity of the proposed theoretical methods.

show abstract

Analysis and Design of Markov Jump Systems with Complex Transition Probabilities

Cited by 46 publications

References 0 publications

Adaptive tracking control for a class of random pure‐feedback nonlinear systems with Markovian switching

Adaptive tracking control for a class of random pure‐feedback nonlinear systems with Markovian switching

Collaborative distributed design for wireless control systems with Markovian‐type control network and distributed network‐induced time delays

Stability Analysis for a Class of Discrete‐Time Nonhomogeneous Markov Jump Systems with Multiplicative Noises

Contact Info

Product

Resources

About