Mayer-Type Optimal Control of Probabilistic Boolean Control Network With Uncertain Selection Probabilities

Toyoda, Mitsuru; Wu, Yuhu

doi:10.1109/tcyb.2019.2954849

Cited by 71 publications

(31 citation statements)

References 41 publications

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“…Step 2: DefineM =L 1 δ 1 2 +L 1 δ 2 2 ∈ B 16×16 . From Proposition 1, 1 = δ ω 1 16 = {δ 1 16 , δ 13 16 }, 2 = δ ω 2 16 = {δ 2 16 , δ 3 16 , δ 14 16 , δ 15 16 }, 3 = δ ω 3 16 = {δ 1 16 , δ 4 16 , δ 5 16 , δ 8 16 , δ 13 16 , δ 16 16 } and 4 = δ ω 4 16 = {δ 2 16 , δ 3 16 , δ 6 16 , δ 7 16 , δ 14 16 , δ 15 16 } are stable in BCN-IE (12). SinceM | ω 1 = 1 2×2 ,M | ω 2 = 1 4×4 ,M | ω 3 = 1 6×6 andM | ω 4 = 1 6×6 , by Proposition 2, 1 and 2 are stable complex attractors.…”

Section: Examplementioning

confidence: 99%

“…Step 1: Denotex(t) = x(t) x(t) andȳ(t) = y(t) ŷ(t). From Lemma 2, the following BCN-IE is derived 1 16 , δ 2 16 , δ 4 16 , δ 5 16 , δ 6 16 , δ 8 16 , δ 9 16 , δ 10 16 , δ 12 16 , δ 15 16 , δ 16 16 }.…”

Section: Examplementioning

confidence: 99%

“…Step 3: Because ∩ 1 = {δ 1 16 } = ∅ and ∩ 2 = {δ 2 16 , δ 15 16 } = ∅, the output of BCN-IE (10) can track the output of reference signal (11) according to Theorem 1.…”

Section: Examplementioning

confidence: 99%

“…Step 4: Based on formula (7), one obtains ϒ 1 = {δ 1 16 , δ 4 16 , δ 5 16 , δ 8 16 , δ 13 16 , δ 16 16 }, ϒ 2 = {δ 2 16 , δ 3 16 , δ 6 16 , δ 7 16 , δ 9 16 , δ 10 16 , δ 11 16 , δ 12 16 , δ 14 16 , δ 15 16 }. Thus, a sequence of state subsets is computed: 1 1 = 1 ∩ = {δ 1 16 }, 1 2 = {δ 4 16 , δ 5 16 , δ 8 16 , δ 13 16 , δ 16 16 }, 1 3 = {δ 9 16 , δ 12 16 }, 2 1 = 2 ∩ = {δ 2 16 , δ 15 16 }, 2 2 = {δ 3 16 , δ 6 16 , δ 7 16 , δ 14 16 } and 2 3 = {δ 10 16 , δ 11 16 }.…”

Section: Examplementioning

confidence: 99%

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Output Tracking of Boolean Control Networks With Impulsive Effects

Feng

2020

IEEE Access

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In this article, the output tracking of Boolean control networks with impulsive effects (BCNs-IE) is discussed. Based on structure matrices of BCNs-IE and controllability matrices with respect to state subsets, stable complex attractors are studied. By constructing an auxiliary BCN-IE and using stable complex attractors, a necessary and sufficient condition for the output tracking problem is proposed. In addition, an algorithm to design state feedback controllers for the output tracking problem is provided. Moreover, an example is given to show the validity of the obtained results. INDEX TERMS Boolean control network, impulsive effect, output tracking, stable complex attractor, state feedback controller.

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Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

“…Step 3: Because ∩ 1 = {δ 1 16 } = ∅ and ∩ 2 = {δ 2 16 , δ 15 16 } = ∅, the output of BCN-IE (10) can track the output of reference signal (11) according to Theorem 1.…”

Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

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Output Tracking of Boolean Control Networks With Impulsive Effects

Feng

2020

IEEE Access

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“…Recently, some notable works focus their research on data association for dealing with the connection between semantic objects and RGB images in dynamic environments (Bowman et al, 2017;Doherty et al, 2019;Yu and Lee, 2018;Ran et al, 2021), and allow for better application of semantic techniques in SLAM algorithms. Furthermore, to deal with the uncertainty of environment, a potential approach is to improve SLAM algorithm by combining with various optimization-based algorithms (Wu and Shen, 2018;Shen et al, 2021;Shen et al, 2020b;Le et al, 2021;Wu et al, 2021;Toyoda and Wu, 2021) for scholastic systems.…”

Section: Introductionmentioning

confidence: 99%

OC-SLAM: Steadily Tracking and Mapping in Dynamic Environments

Deng

et al. 2021

Front. Energy Res.

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Visual Simultaneous Localization and Mapping (SLAM) system is mainly used in real-time localization and mapping tasks of robots in various complex environments, while traditional monocular vision algorithms are struggling to cope with weak texture and dynamic scenes. To solve these problems, this work presents an object detection and clustering assisted SLAM algorithm (OC-SLAM), which adopts a faster object detection algorithm to add semantic information to the image and conducts geometrical constraint on the dynamic keypoints in the prediction box to optimize the camera pose. It also uses RGB-D camera to perform dense point cloud reconstruction with the dynamic objects rejected, and facilitates European clustering of dense point clouds to jointly eliminate dynamic features combining with object detection algorithm. Experiments in the TUM dataset indicate that OC-SLAM enhances the localization accuracy of the SLAM system in the dynamic environments compared with original algorithm and it has shown impressive performance in the localizition and can build a more precise dense point cloud map in dynamic scenes.

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Output feedback stabilization of logical dynamical systems with state‐dependent control constraints

Dai,

Guo

2024

Asian Journal of Control

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Control constraints are common in real‐world applications. However, when the control constraint depends on the system state, designing a stabilizing output feedback that adheres to the constraint can be challenging if the state is unmeasurable. This study proposes a novel dual‐loop structural output feedback for logical dynamical systems (LDSs) with state‐dependent control constraints. The inner‐loop controller is a pre‐output feedback designed based on the state‐dependent constraint, which always selects a common admissible control for all possible states. A system with free control input is obtained by combining the pre‐output feedback with the LDS. The outer‐loop controller, an ordinary output feedback, is then designed for the combined system. It is proven that any admissible time‐invariant output feedback can be decomposed into a pre‐output feedback and an ordinary output feedback. Furthermore, a necessary and sufficient condition for stabilizability by admissible output feedback is obtained. Finally, an example is presented to demonstrate the effectiveness of the obtained results.

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Mayer-Type Optimal Control of Probabilistic Boolean Control Network With Uncertain Selection Probabilities

Cited by 71 publications

References 41 publications

Output Tracking of Boolean Control Networks With Impulsive Effects

Output Tracking of Boolean Control Networks With Impulsive Effects

OC-SLAM: Steadily Tracking and Mapping in Dynamic Environments

Output feedback stabilization of logical dynamical systems with state‐dependent control constraints

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