A data-driven adaptability control method on the complex system of water resources

2011 ◽  
Author(s):  
Wei Xiaodong ◽  
Xu Lizhong ◽  
Wang Fengzhong ◽  
Ma Zhenli ◽  
Li Chenming ◽  
...  
Keyword(s):  
Complex System ◽  
Water Resources ◽  
Control Method ◽  
Data Driven

Data-driven tracking consensus for a class of unknown nonlinear multi-agent systems

2021 ◽  
pp. 107754632110340
Author(s):  
Jia Wu ◽  
Ning Liu ◽  
Wenyan Tang
Keyword(s):  
Control Method ◽  
Sufficient Conditions ◽  
Data Driven ◽  
The Novel ◽  
Multi Agent Systems ◽  
Tracking Errors ◽  
Agent Systems ◽  
Model Free ◽  
Strongly Connected ◽  
Multi Agent

This study investigates the tracking consensus problem for a class of unknown nonlinear multi-agent systems A novel data-driven protocol for this problem is proposed by using the model-free adaptive control method To obtain faster convergence speed, one-step-ahead desired signal is introduced to construct the novel protocol Here, switching communication topology is considered, which is not required to be strongly connected all the time Through rigorous analysis, sufficient conditions are given to guarantee that the tracking errors of all agents are convergent under the novel protocol Examples are given to validate the effectiveness of results derived in this article

Chaotic motion control of a vibro-impact system based on data-driven control method

2021 ◽  
pp. 107754632110433
Author(s):  
Xiao-juan Wei ◽  
Ning-zhou Li ◽  
Wang-cai Ding
Keyword(s):  
Motion Control ◽  
Data Model ◽  
Chaotic Motion ◽  
Control Method ◽  
Data Driven ◽  
Damping Coefficient ◽  
Input Output ◽  
Output Data ◽  
Controlled System ◽  
Impact System

For the chaotic motion control of a vibro-impact system with clearance, the parameter feedback chaos control strategy based on the data-driven control method is presented in this article. The pseudo-partial-derivative is estimated on-line by using the input/output data of the controlled system so that the compact form dynamic linearization (CFDL) data model of the controlled system can be established. And then, the chaos controller is designed based on the CFDL data model of the controlled system. And the distance between two adjacent points on the Poincaré section is used as the judgment basis to guide the controller to output a small perturbation to adjust the damping coefficient of the controlled system, so the chaotic motion can be controlled to a periodic motion by dynamically and slightly adjusting the damping coefficient of the controlled system. In this method, the design of the controller is independent of the order of the controlled system and the structure of the mathematical model. Only the input/output data of the controlled system can be used to complete the design of the controller. In the simulation experiment, the effectiveness and feasibility of the proposed control method in this article are verified by simulation results.

scholarly journals A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Sheng Hu ◽  
Shuanjun Song ◽  
Wenhui Liu
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Control Method ◽  
Cloud Model ◽  
Data Driven ◽  
Monitoring Method ◽  
Control Approach ◽  
Acceptable Method ◽  
And Performance ◽  
Model Similarity

Considering the problem that the process quality state is difficult to analyze and monitor under manufacturing big data, this paper proposed a data cloud model similarity-based quality fluctuation monitoring method in data-driven production process. Firstly, the randomness of state fluctuation is characterized by entropy and hyperentropy features. Then, the cloud pool drive model between quality fluctuation monitoring parameters is built. On this basis, cloud model similarity degree from the perspective of maximum fluctuation border is defined and calculated to realize the process state analysis and monitoring. Finally, the experiment is conducted to verify the adaptability and performance of the cloud model similarity-based quality control approach, and the results indicate that the proposed approach is a feasible and acceptable method to solve the process fluctuation monitoring and quality stability analysis in the production process.

Applications of Data-Driven Modelling and Machine Learning in Control of Water Resources

2011 ◽  
pp. 197-217 ◽  
Author(s):  
D. P. Solomatine
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
Computational Intelligence ◽  
Flood Control ◽  
Water Levels ◽  
Data Driven ◽  
Real Time Control ◽  
Modeling And Control ◽  
Physically Based ◽  
And Control

Traditionally, management and control of water resources is based on behavior-driven or physically based models based on equations describing the behavior of water bodies. Since recently models built on the basis of large amounts of collected data are gaining popularity. This modeling approach we will call data-driven modeling; it borrows methods from various areas related to computational intelligence—machine learning, data mining, soft computing, etc. The chapter gives an overview of successful applications of several data-driven techniques in the problems of water resources management and control. The list of such applications includes: using decision trees in classifying flood conditions and water levels in the coastal zone depending on the hydrometeorological data, using artificial neural networks (ANN) and fuzzy rule-based systems for building controllers for real-time control of water resources, using ANNs and M5 model trees in flood control, using chaos theory in predicting water levels for ship guidance, etc. Conclusions are drawn on the applicability of the mentioned methods and the future role of computational intelligence in modeling and control of water resources.

scholarly journals Energy-saving irrigation management

2018 ◽  
Vol 245 ◽  
pp. 06014 ◽  
Author(s):  
Sergey Andreev ◽  
Vladimir Zaginaylov ◽  
Andris Matveev
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
Control Method ◽  
Strong Dependence ◽  
Irrigation Management ◽  
Vegetation Period ◽  
Weather Factors ◽  
Measuring Devices ◽  
Management Method

A significant part of the water resources used in agricultural production comes for irrigation. Due to the strong dependence of soil moisture on weather factors, the irrigation process must be carefully managed. To date, irrigation management is mainly carried out either as a function of soil moisture or according to a predetermined program. The article shows that both methods of management are imperfect since they can lead to a violation of agrotechnical requirements and waste of water. In order to improve the quality of irrigation and eliminate overspending of water resources, it was proposed to manage the water in a combined way. In this case, the formation of the control action occurs according to the results of the comparison of the controlled variable and disturbing influences with the reference values. The controlled value is the soil moisture, and as disturbing influences are considered the temperature and humidity of the air, atmospheric pressure, wind speed, precipitation. In addition, the proposed irrigation management method takes into account the forecast of the synoptic services on precipitation, their intensity and duration. To obtain information on the controlled value, as well as on disturbing influences, appropriate measuring devices are used, and information on the prediction of precipitation is delivered from a specialized server via the Internet. Before starting to use the control method, the agrotechnical requirements, the type, age and vegetation period of the plants are determined and set. The inclusion of irrigation equipment is carried out in accordance with the program and shut down - depending on the magnitude of the control signal.

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