Stabilization of NCSs by random allocation of transmission power to sensors

Wang, Liyuan; Guo, Ge; Zhuang, Yan

doi:10.1007/s11432-016-5563-3

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…A numerical example has been given to illustrate our results. Future research could include power constraint [20] and data rate limit [21] in this framework.…”

Section: Discussionmentioning

confidence: 99%

Stochastic Channel Allocation for Nonlinear Systems with Markovian Packet Dropout

et al. 2018

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This paper addresses a channel scheduling problem for group of dynamically decoupled nonlinear subsystems with actuators connected through digital communication channels and controlled by a centralized controller. Due to the limited communication capacity, only one channel can be activated and hence there is only one pair of sensor and actuator can communicate with the controller at each time instant. In addition, the communication channels are not reliable so Markovian packed dropout is introduced. A predictive control framework is adopted for controller/scheduler co-design to alleviate the performance loss caused by the limited communication capacity. Instead of sending a single control value, the controller sends a sequence of predicted control values to a selected actuator so that there are control input candidates which can be fed to the subsystem when the actuator does not communicate with the controller. A stochastic algorithm is proposed to schedule the usage of the communication medium and sufficient conditions on stochastic stability are given under some mild assumptions.

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“…A numerical example has been given to illustrate our results. Future research could include power constraint [20] and data rate limit [21] in this framework.…”

Section: Discussionmentioning

confidence: 99%

Stochastic Channel Allocation for Nonlinear Systems with Markovian Packet Dropout

et al. 2018

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“…Stability is the most important requirement in distributed filter design for a networked sensor system with an energy constraint [45]. Because the information cannot be shared to the extent possible in an ideal system, the stability of the system is therefore of greater practical interest.…”

Section: Stability Analysis Under Optimal Communication Protocolmentioning

confidence: 99%

Distributed filtering for time-varying networked systems with sensor gain degradation and energy constraint: a centralized finite-time communication protocol scheme

Zhao

Zhou

2018

Sci. China Inf. Sci.

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This paper focuses on the distributed filtering problem for a class of time-varying networked systems with sensor gain degradation and energy constrained communication protocol. To satisfy the requirement of power consumption and reduce the schedule computing complexity, centralized cyclic finite-time communication strategy optimization is taken into account. The networked system considered in this paper consists of spatially distributed sensors linked to their neighbor sensors, where each sensor node suffers from different gain degradation, and the transmission decisions of all the communication channels obey the centralized transmission schedule strategy identically. First, we present scattered communication action based on single-sensor transmission modeling with an energy constraint. Subsequently, an optimal communication protocol considering expected average error covariance is derived between the target system and each sensor node over the distributed sensor systems, based on a centralized finite-time scheme. Finally, by transforming the overall estimation error covariance of the systems at each sampling time into quadratic form, a conditionally unbiased least-square recursive distributed filtering technique over the networked system is designed at each sensor node. The system stability condition under such an optimal schedule is also accomplished with bounded covariance. A numerical example is provided to demonstrate the utility and effectiveness of the distributed filtering technique using the proposed optimized energy constrained communication protocol.

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“…It is noted from Definition 1 that the equilibrium control is local optimal. Specifically, from (5) we see that To guarantee the solvability of Problem 1, we make the following standard assumption on weighting matrices of (2). Assumption 1.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Time consistency means that the optimal control is independent of the initial pair, i.e., if the control is optimal on the full time interval, it is also optimal on any time subinterval. The Bellman optimality principle from the dynamic programming methods serves as a basic tool in seeking the optimal control for the time-consistent stochastic control problems, see [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Time-inconsistent stochastic linear quadratic control for discrete-time systems

Zhang

2017

Sci. China Inf. Sci.

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This paper is mainly concerned with the time-inconsistent stochastic linear quadratic (LQ) control problem in a more general formulation for discrete-time systems. The time-inconsistency arises from three aspects: the coefficient matrices depending on the initial pair, the terminal of the cost function involving the initial pair together with the nonlinear terms of the conditional expectation. The main contributions are: firstly, the maximum principle is derived by using variational methods, which forms a flow of forward and backward stochastic difference equations (FBSDE); secondly, in the case of the system state being one-dimensional, the equilibrium control is obtained by solving the FBSDE with feedback gain based on several nonsymmetric Riccati equations; finally, the necessary and sufficient solvability condition for the time-inconsistent LQ control problem is presented explicitly. The key techniques adopted are the maximum principle and the solution to the FBSDE developed in this paper.

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Stabilization of NCSs by random allocation of transmission power to sensors

Cited by 8 publications

References 19 publications

Stochastic Channel Allocation for Nonlinear Systems with Markovian Packet Dropout

Stochastic Channel Allocation for Nonlinear Systems with Markovian Packet Dropout

Distributed filtering for time-varying networked systems with sensor gain degradation and energy constraint: a centralized finite-time communication protocol scheme

Time-inconsistent stochastic linear quadratic control for discrete-time systems

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