Real-Time Reachable Set Control for Singular Markov Jump Networked Cascade Systems

Zhang, Liang; Zong, Guangdeng; Zhao, Xudong; Zhao, Ning

doi:10.1109/tcsii.2021.3099913

Cited by 15 publications

(11 citation statements)

References 19 publications

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“…where x ∈ R n , and Γ ∈ S n + is designed to be a weight matrix. As is shown in (15), if the value of s(z k , Γ) is smaller, the probability of γ k is lower at each time k. Under this situation, the communication between sensor and estimator is more likely to be triggered.…”

Section: Remote State Estimationmentioning

confidence: 94%

“…According to the MMSE estimator E * considered in Theorem 1 and ∆ given in (15). We can calculate the estimator's estimation error covariance P k as follows:…”

Section: Theoremmentioning

confidence: 99%

“…We further compare the transmission probabilities with different Γ. 15), it follows that we have the following:…”

Section: Periodic Sending Strategymentioning

confidence: 99%

“…When sensors can harvest energy to overcome energy constraints, Huang et al applied an event-triggered estimator to NCSs in [14]. The problem of real-time reachable set control for a class of singular Markov jump networked cascade systems with time-delay, disturbance and non-zero initial conditions, was considered in [15]. For subsequent results of similar studies, see also [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Periodic Event-Triggered Estimation for Networked Control Systems

et al. 2021

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This paper considers the problem of remote state estimation in a linear discrete invariant system, where a smart sensor is utilized to measure the system state and generate a local estimate. The communication depends on an event scheduler in the smart sensor. When the channel between the remote estimator and the smart sensor is activated, the remote estimator simply adopts the estimate transmitted by the smart sensor. Otherwise, it calculates an estimate based on the available information. The closed-form of the minimum mean-square error (MMSE) estimator is introduced, and we use Gaussian preserving event-based sensor scheduling to obtain an ideal compromise between the communication cost and estimation quality. Furthermore, we calculate a variation range of communication probability, which helps to design the policy of event-triggered estimation. Finally, the simulation results are given to illustrate the effectiveness of the proposed event-triggered estimator.

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Section: Remote State Estimationmentioning

confidence: 94%

“…According to the MMSE estimator E * considered in Theorem 1 and ∆ given in (15). We can calculate the estimator's estimation error covariance P k as follows:…”

Section: Theoremmentioning

confidence: 99%

“…We further compare the transmission probabilities with different Γ. 15), it follows that we have the following:…”

Section: Periodic Sending Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Periodic Event-Triggered Estimation for Networked Control Systems

et al. 2021

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“…Although this mode could transmit control signals to the actuator in real‐time, it is likely to lead to excessive consumptions of both communication and computation resources 34‐39 . Different from the time‐triggered control, the event‐triggered control determines online when to communicate by a triggered mechanism, updating the values of control inputs only if the deviation between the current state and the desired state exceeds a predefined threshold 40‐44 . In this sense, the event‐triggered control is more advantageous in communication efficiency and has been employed for improving control law design of fractional‐order nonlinear systems 45 .…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered adaptive tracking control for uncertain fractional‐order nonstrict‐feedback nonlinear systems via command filtering

Wang

Zhao

et al. 2022

Intl J Robust & Nonlinear

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This article puts forward an event-triggered adaptive neural tracking control strategy for a class of uncertain fractional-order nonstrict-feedback nonlinear systems with full-state constraints and dead-zone inputs. The proposed control strategy is capable of surmounting an inherent shortcoming in most traditional backstepping control approaches which results from the repeated differentiation of virtual control functions, and enhancing the transmission efficiency of control signals by introducing an event-triggered mechanism with a dynamic threshold condition. Furthermore, by constructing a novel barrier Lyapunov function in each step of the backstepping procedure, the design difficulty arising from the system state constraints issue under fractional-order cases is resolved. It is strictly proved that the tracking error can converge into a desired small neighborhood of the origin and all signals in the closed-loop system are bounded. Finally, a simulation example is given to verify the effectiveness of our proposed control strategy.

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Fuzzy‐based adaptive control design for stochastic nonlinear time‐delay systems via event‐triggered mechanism

Liu

Zeng

Zhu

et al. 2022

Adaptive Control & Signal

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Summary This article elaborates an adaptive fuzzy event‐triggered control design for stochastic nonlinear time‐delay systems with full‐state constraints. The prescribed constraints are ensured by employing barrier Lyapunov function. The fuzzy logic systems are exploited to approximate lumped unknown nonlinear functions. By cleverly choosing Lyapunov–Krasovskii functional, the adverse effect of time‐delay is effectively overcome. Meanwhile, the event‐triggered mechanism is adopted to save communication resource. It is illustrated that all signals of the controlled system are bounded and all states are maintained the prescribed regions at every step of recursive backstepping design. Finally, simulation results elucidate the validity of the devised scheme.

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Real-Time Reachable Set Control for Singular Markov Jump Networked Cascade Systems

Cited by 15 publications

References 19 publications

Periodic Event-Triggered Estimation for Networked Control Systems

Periodic Event-Triggered Estimation for Networked Control Systems

Event‐triggered adaptive tracking control for uncertain fractional‐order nonstrict‐feedback nonlinear systems via command filtering

Fuzzy‐based adaptive control design for stochastic nonlinear time‐delay systems via event‐triggered mechanism

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