Observer-based passive control of non-homogeneous Markov jump systems with random communication delays

Chen, Yun; Chen, Zhangping; Chen, Zhenyu; Xue, Anke

doi:10.1080/00207721.2020.1752844

Cited by 55 publications

(15 citation statements)

References 55 publications

(66 reference statements)

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“…(2) the estimation of EV SOC based on the measurement with the network induced phenomenon [4,31,32,37]; (3) the EV charging station planning problem subject to incomplete information [19,23,25,28,38,43]; (4) the cooperative control of multiple vehicles based on the multiagent systems [7]; and…”

Section: Discussionmentioning

confidence: 99%

Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm

Bai

Wang

Zou

et al. 2021

Complex Intell. Syst.

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This paper is concerned with the electric vehicle (EV) charging station planning problem based on the dynamic charging demand. Considering the dynamic charging behavior of EV users, a dynamic prediction method of EV charging demand is proposed by analyzing EV users’ travel law via the trip chain approach. In addition, a multi-objective charging station planing problem is formulated to achieve three objectives: (1) maximize the captured charging demands; (2) minimize the total cost of electricity and the time consumed for charging; and (3) minimize the load variance of the power grid. To solve such a problem, a novel method is proposed by combining the hybrid particle swarm optimization (HPSO) algorithm with the entropy-based technique for order preference by similarity to ideal solution (ETOPSIS) method. Specifically, the HPSO algorithm is used to obtain the Pareto solutions, and the ETOPSIS method is employed to determine the optimal scheme. Based on the proposed method, the siting and sizing of the EV charging station can be planned in an optimal way. Finally, the effectiveness of the proposed method is verified via the case study based on a test system composed of an IEEE 33-node distribution system and a 33-node traffic network system.

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Section: Discussionmentioning

confidence: 99%

Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm

Bai

Wang

Zou

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Then the results that may appear in the data set, that is, the expression results we need to classify. Similar to the method of calculating the gain matrices (Chen et al 2020), the information it contains can be represented by the concept of information entropy, and the calculation method is shown in formula (20).…”

Section: Vector Triangle Scale and Region Selectionmentioning

confidence: 99%

Texture feature extraction and optimization of facial expression based on weakly supervised clustering

Tang

Mao

Sheng

et al. 2021

Systems Science & Control Engineering

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In order to improve the recognition rate of weak annotation data in facial expression recognition task, this paper proposes a multi-scale and multi-region vector triangle texture feature extraction scheme based on weakly supervised clustering algorithm. According to the information gain rate of extracted features, combined with threshold selection and random dropout strategy, the best selection of vector triangle texture feature scale is explored, and the feature space is optimized under the premise of sufficient feature space information, the reduction of feature space is realized and the information redundancy is reduced. For the positive and negative expression units, the facial expression images in the data set are divided into two categories. The positive and negative facial expressions are taken to form the same kind of samples, the positive and negative facial expressions are taken to form the positive and negative samples, and the annotation labels are taken to form the weak annotation labels. The experimental results show that the best recognition rate of the proposed scheme is 84.1%, which is 5.8% higher than the unoptimized texture feature scheme.

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“…On another research frontier, Markovian jump systems (MJSs) have received much attention in recent years owing to their great capability of describing the plants suffering from some unexpected accidents or random changes such as the stochastically occurring environmental disturbances, the components failures, the sudden change in the interconnection between subsystems and the abrupt changes at the operating point of a nonlinear plant 18‐20 . The central idea of MJSs is to control the switching between dynamic modes through a set of random switching signals characterized by a Markov process/chain.…”

Section: Introductionmentioning

confidence: 99%

Efficient model predictive control for Markovian jump systems with Lur'e nonlinear term: A dual‐mode control scheme

Zhang

Song

2021

Intl J Robust & Nonlinear

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In this article, the efficient model predictive control (MPC) problem is investigated for a class of Markovian jump Lur'e systems (MJLSs) subject to parameter uncertainties and input constraints, where each subsystem is described by the combination of a linear part and a nonlinear Lur'e term. The main purpose of the addressed problem is to design a set of dual‐mode feedback controllers in the framework of efficient MPC to make a nice tradeoff among the online computation burden, the initial feasible region, and the control performance. To this aim, the following two tasks need to be fulfilled: 1) for the terminal constraint set, the corresponding fixed feedback gain that is composed of a linear part and a nonlinear part is designed with aid of the Lur'e‐type Lyapunov‐like function related to the modes of subsystems; and 2) a fairly large initial feasible region is obtained off‐line by adjusting the dimension of the control perturbation sequence. Then, an online optimization problem is put forward to design this perturbation sequence with the determined dimension to steer the system state into the terminal constraint set within the pre‐determined steps. Sufficient conditions are presented to guarantee the recursive feasibility of the proposed efficient MPC algorithm and the mean‐square stability of the underlying MJLSs. Finally, a simulation example with regards to the DC motor device system is provided to demonstrate the effectiveness of our proposed MPC strategy.

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Observer-based passive control of non-homogeneous Markov jump systems with random communication delays

Cited by 55 publications

References 55 publications

Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm

Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm

Texture feature extraction and optimization of facial expression based on weakly supervised clustering

Efficient model predictive control for Markovian jump systems with Lur'e nonlinear term: A dual‐mode control scheme

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