Research on the control method for the torque of wind generator based on data-driven

2015 ◽  
pp. 365-368
Author(s):  
T Li ◽  
X Hou ◽  
H Lin ◽  
L Zhao ◽  
H Liu ◽  
...  
Keyword(s):  
Control Method ◽  
Data Driven ◽  
Wind Generator

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.

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