A Novel Architecture of a Six Degrees of Freedom Parallel Platform

Gu, Qiuxiang; Tian, Jiawei; Yang, Bo; Liu, Mingzhe; Gu, Borui; Yin, Zhengtong; Yin, Lirong; Zheng, Wenfeng

doi:10.3390/electronics12081774

Cited by 55 publications

(22 citation statements)

References 34 publications

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“…These numerical experiments illustrate that the NODGM model actually forecasts short-dated control conditions for nonlinear systems in the real world. Therefore, our model can be used to predict all practical applications for current studies, [66][67][68][69] which is also believed robust in engineering systems and decision-making. Nevertheless, this model still has two problems.…”

Section: Discussionmentioning

confidence: 99%

Neural ordinary differential gray algorithm to forecasting models of controlled systems

Meng

Jiang

et al. 2023

International Journal of Advanced Robotic Systems

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Due to the feasibility of the gray model for predicting time series with small samples, the gray theory is well investigated since it is presented and is currently evolved in an important manner for forecasting small samples. This study proposes a new gray prediction criterion based on the neural ordinary differential equation, which is named the neural ordinary differential gray mode. This neural ordinary differential gray mode permits the forecasting model to be learned by a training process which contains a new whitening equation. It is needed to prepare the structure and time series, compared with other models, according to the regularity of actual specimens in advance, therefore this model of neural ordinary differential gray mode can provide comprehensive applications as well as learning the properties of distinct data specimens. To acquire a better model which has highly predictive efficiency, afterward, this study trains the model by neural ordinary differential gray mode using the Runge–Kutta method to obtain the prediction sequence and solve the model. The controller establishes an advantageous theoretical foundation in adapting to novel wheels and comprehensively spreads the utilize extent of mechanical elastic vehicle wheel.

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Section: Discussionmentioning

confidence: 99%

Neural ordinary differential gray algorithm to forecasting models of controlled systems

Meng

Jiang

et al. 2023

International Journal of Advanced Robotic Systems

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“…To consider the optimization problem in a network, the grid should include smart devices [57][58][59][60][61][62][63][64][65]. These systems are based on telecommunications equipment [66][67][68][69][70][71][72][73].…”

Section: Mathematical Modelmentioning

confidence: 99%

Energy Management Capability in the Reconfigurable Distribution Networks with Distributed Generation for Minimization of Energy Loss

Jiang

Zhang

2023

Applied Sciences

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One of the approaches to improve operation indices, such as power loss and reliability, is to smoothen voltage profiles. Energy management of generation units and network reconfiguration are two methods to achieve this. Hence, the latter is presented in this paper in a distribution with distributed generation units. To this end, an objective function is formulated aiming to minimize power loss and enhance the operating situation of the network. This problem is subject to various constraints and limits such as AC power flow equations, operation limits, planning model, and operation model of distributed generations. It is an integer nonlinear optimization problem and is solved using the crow search algorithm and the optimal solution is obtained. Two major innovations of the study include modeling generation units’ operation and network configuration mathematical expression of operation indicators. The method is applied to a test system and results demonstrate the high performance of this approach in improving the operation of the network. Finding the solution in less time with a satisfying standard deviation are two advantages of the proposed algorithm. Adopting this method, the network operator can reduce power loss through proper management of distributed generation power and optimal scheduling of switches. Moreover, the obtained voltage profile is more desirable in comparison to that of power flow studies.

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“…Numerous studies have been undertaken regarding energy optimization in the SPG, covering various aspects and viewpoints such as dynamic pricing policies [12], recognition methods [13], DFT phasor estimators [14], control algorithms [15], etc. However, RES modelling, scheduling, and optimization are necessary before addressing the above aspects and viewpoints in the literature.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Alzahrani

Hafeez

Ali³

et al. 2023

Sustainability

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Multi-objective energy optimization is indispensable for energy balancing and reliable operation of smart power grid (SPG). Nonetheless, multi-objective optimization is challenging due to uncertainty and multi-conflicting parameters at both the generation and demand sides. Thus, opting for a model that can solve load and distributed energy source scheduling problems is necessary. This work presents a model for operation cost and pollution emission optimization with renewable generation in the SPG. Solar photovoltaic and wind are renewable energy which have a fluctuating and uncertain nature. The proposed system uses the probability density function (PDF) to address uncertainty of renewable generation. The developed model is based on a multi-objective wind-driven optimization (MOWDO) algorithm to solve a multi-objective energy optimization problem. To validate the performance of the proposed model a multi-objective particle swarm optimization (MOPSO) algorithm is used as a benchmark model. Findings reveal that MOWDO minimizes the operational cost and pollution emission by 11.91% and 6.12%, respectively. The findings demonstrate that the developed model outperforms the comparative models in accomplishing the desired goals.

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A Novel Architecture of a Six Degrees of Freedom Parallel Platform

Cited by 55 publications

References 34 publications

Neural ordinary differential gray algorithm to forecasting models of controlled systems

Neural ordinary differential gray algorithm to forecasting models of controlled systems

Energy Management Capability in the Reconfigurable Distribution Networks with Distributed Generation for Minimization of Energy Loss

Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

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