Endec-decoder-based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e97" altimg="si4.svg"><mml:mi>N</mml:mi></mml:math>-step model predictive control: Detectability, stability and optimization

Song, Yan; Wang, Zidong; Zou, Lei; Shuai, Liu

doi:10.1016/j.automatica.2021.109961

Cited by 23 publications

(7 citation statements)

References 54 publications

(67 reference statements)

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“…Compared with continuous wavelet transform, DWT can represent the original signal as coefficients with different levels of approximation coefficients and detail coefficients, and DWT parameters affect the robustness of the classifier towards noises and overall classification accuracy [73]. It was pointed out by [74] that DWT can reduce the required storage space.…”

Section: Figure 6: Discrete Wavelet Decomposition Vectorsmentioning

confidence: 99%

“…Wavelet transform and S transform can be utilized together with RF in [70] to show that RF classifiers based on wavelet transform WT better performance classifying data. Literature [73] used RF to develop DWT to extract features to build a model. Literature [30] combined WT and ST extraction to establish a decision tree classifier that can effectively identify mixed interference signals.…”

Section: Random Forestsmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field, where specific scenarios and events for which each technique is applicable are also clearly presented. Finally, comments on the future evolution of PQD detection techniques are given. Unlike the published review articles, this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

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Section: Figure 6: Discrete Wavelet Decomposition Vectorsmentioning

confidence: 99%

Section: Random Forestsmentioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…[5][6][7][8][9][10] These studies mainly cover the analysis and synthesis of dynamic behaviors, such as stability, controllability, reachability, and controller design with desired system performance. [11][12][13][14][15][16][17] For instance, a novel hybrid control scheme was proposed to realize the exponential synchronization of a class of state-dependent SNNs with distributed delays. 12 By employing the pole assignment method, Reference 13 studied the region stabilization issue of SNNs.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, SNNs have recently attracted more and more attention of researchers on account of their more flexible structure and higher computing speed compared to conventional neural networks 5‐10 . These studies mainly cover the analysis and synthesis of dynamic behaviors, such as stability, controllability, reachability, and controller design with desired system performance 11‐17 . For instance, a novel hybrid control scheme was proposed to realize the exponential synchronization of a class of state‐dependent SNNs with distributed delays 12 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic event‐triggered control for delayed switched neural networks: A merging signal scheme

Wang,

Huang

et al. 2023

Intl J Robust & Nonlinear

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This paper investigates the asynchronous control of delayed switched neural networks via a dynamic event‐triggering mechanism (DETM) and a merging signal scheme. Firstly, an improved DETM is proposed through adding an exponential decaying term into the triggering condition, which is proved to have a larger lower bound by comparison with the static event‐triggering mechanism (SETM) and the existing DETM. Next, a merging signal is constructed by introducing a time‐varying delay to combine the system switching signal with the controller switching signal, which is convenient to deal with the synchronous and asynchronous switching in a unified framework. Then, the closed‐loop system which integrates the proposed DETM with the merging signal is formulated. Moreover, by constructing multiple time‐dependent Lyapunov functionals, sufficient conditions which ensure the asymptotical stability of the resulting closed‐loop system are derived, and a design algorithm for the feedback controller is designed consequently. Finally, the validity and superiority of the proposed results are demonstrated by numerical examples.

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“…Predictive control, a typical digital control strategy, has become an attractive research topic since it emerged originally from the industry in the 1970s, due primarily to its advanced capability of handling multi‐variable control problems with hard constraints 1‐4 . Model predictive control (MPC) based on the state space model, proposed at the end of the last century, is one of the main branches of predictive control, which develops a technical methodology for control synthesis by using mathematical techniques.…”

Section: Introductionmentioning

confidence: 99%

Dynamic output feedback model predictive control for asynchronous Markovian jump systems under try‐once‐discard protocol

Yin

Song

Zhang

et al. 2023

Intl J Robust & Nonlinear

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This paper is concerned with the dynamic output feedback robust model predictive control (RMPC) problem for a class of discrete‐time asynchronous Markovian jump systems (MJSs) subject to polytopic parameter uncertainties and hard constraints on states and inputs. A hidden Markov model is employed to describe the asynchronous phenomenon between the detected modes and the system modes. For the purpose of improving the network utilization as well as reducing the transmission burden and avoiding data collisions, the try‐once‐discard (TOD) protocol is deployed in the shared communication network between sensor nodes and the controller node. Considering the difficulty of obtaining the system state in the practice, the dynamic output‐feedback control in the framework of RMPC is adopted, and the worst‐case optimization problem over the infinite moving horizon is formulated for the performance analysis and control synthesis. By means of the Lyapunov‐like function approach, free weighting matrices and inequality techniques such as the ‐procedure are introduced to deal with the non‐convexity caused by couplings between decisive variables and the negative influence resulting from the data orchestration is mitigated. Based on the delicate establishments, a certain upper bound of the objective function is employed to construct an auxiliary optimization problem with solvability to find the desired controllers, and sufficient conditions are obtained to guarantee that the MJSs under the proposed RMPC‐based controllers are mean‐square stable. Finally, an illustrative example is used to demonstrate the validity of the proposed methods.

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Endec-decoder-based N-step model predictive control: Detectability, stability and optimization

Cited by 23 publications

References 54 publications

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Dynamic event‐triggered control for delayed switched neural networks: A merging signal scheme

Dynamic output feedback model predictive control for asynchronous Markovian jump systems under try‐once‐discard protocol

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