scholarly journals Neuro-Knowledge Model Based on a PID Controller to Automatic Steering of Ships

10.5772/50316 ◽  
2012 ◽  
Author(s):  
Jose Luis Calvo Rolle ◽  
Hector Quintian
Keyword(s):  
Pid Controller ◽  
Knowledge Model ◽  
Model Based ◽  
Automatic Steering

ON HIDDEN COUPLING IN MULTIPLE MODEL BASED PID CONTROLLER NETWORKS

2002 ◽  
Vol 35 (1) ◽  
pp. 229-234
Author(s):  
Qinghao Rong ◽  
Seán F. McLoone ◽  
George W. Irwin
Keyword(s):  
Pid Controller ◽  
Multiple Model ◽  
Model Based

Disturbance Observer Based Control Design for Wind Turbine Speed Regulation

2016 ◽  
Author(s):  
Yuan Yuan ◽  
Xu Chen ◽  
Jiong Tang
Keyword(s):  
Wind Turbine ◽  
Pid Controller ◽  
Disturbance Observer ◽  
Control Loop ◽  
Wind Fields ◽  
Model Mismatch ◽  
Model Based ◽  
Speed Regulation ◽  
Power Regulation ◽  
Turbine Speed

Disturbance observer based (DOB) control has been implemented in motion control to reject unknown or time-varying disturbances. In this research, an internal model-based disturbance observer (DOB) design combined with a PID type feedback controller is formulated for wind turbine speed and power regulation. The DOB controller facilitates model-based estimation and cancellation of disturbance using an inner feedback control loop. The disturbance observer combined with a compensator is further designed to deal with the model mismatch. The proposed method is applied to National Renewable Energy laboratory (NREL) offshore 5-MW wind turbine. Our case studies show that the DOB controller can achieve improved speed and power regulation compared to the baseline PID controller, and exhibit excellent robustness under different turbulent wind fields.

scholarly journals Analysis of Model-Based Tuning Method of PID Controller for Excitation Systems Considering Measurement Delay

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 939
Author(s):  
Kyoung-Min Choo ◽  
Chung-Yuen Won
Keyword(s):  
Pid Controller ◽  
Measurement Method ◽  
System Model ◽  
Simplified Model ◽  
Pid Controllers ◽  
Voltage Measurement ◽  
Conventional Model ◽  
Tuning Method ◽  
Model Based ◽  
Measurement Delay

In this paper, a model-based tuning method for a PID controller of excitation systems based on a simplified model that considers measurement delay is proposed. The conventional model-based tuning method, which has been studied previously, uses a simplified excitation system model that ignores all the delay components. However, since the rms voltage measurement can take hundreds of milliseconds to calculate depending on the system settings, this delay cannot be ignored when the required response needs to be as fast as the measurement delay. Furthermore, the linearity of the measurement method is not taken into account because the measurement delay has already been ignored. Therefore, in this paper, a simplified model that considers measurement delay and its linearity is proposed, and a model-based tuning method of PID controllers for two kinds of excitation systems is proposed and compared with the conventional method by analysis. To verify the analysis and proposed tuning method, experiments are conducted for both excitation systems.

A Knowledge Model of Digital Evidence Review Elements Based on Ontology

2020 ◽  
pp. 281-290
Author(s):  
Ning Wang
Keyword(s):  
Knowledge Base ◽  
Domain Ontology ◽  
Digital Evidence ◽  
Knowledge Model ◽  
Ontology Construction ◽  
Model Based ◽  
Application Ontology ◽  
Evidence Review ◽  
Ontology Application ◽  
Knowledge Classification

As existing methods cannot express, share, and reuse the digital evidence review information in a unified manner, a solution of digital evidence review elements knowledge base model based on ontology is presented. Firstly, combing with the multi-source heterogeneous characteristic of digital evidence review knowledge, classification and extraction are accomplished. Secondly, according to the principles of ontology construction, the digital evidence review elements knowledge base model which includes domain ontology, application ontology, and atomic ontology is established. Finally, model can effectively acquire digital evidence review knowledge by analyzing review scenario.

Sign in / Sign up

Export Citation Format

Share Document