An adjustable pitch control system in a large wind turbine based on a fuzzy-PID controller

SPEEDAM 2010 ◽  
2010 ◽  
Author(s):  
Z. L. Dou ◽  
M. Z. Cheng ◽  
Z. B. Ling ◽  
X. Cai
Keyword(s):  
Control System ◽  
Wind Turbine ◽  
Pid Controller ◽  
Fuzzy Pid ◽  
Pitch Control ◽  
Fuzzy Pid Controller ◽  
Large Wind

scholarly journals The fuzzy-PID based-pitch angle controller for small-scale wind turbine

2020 ◽  
Vol 11 (1) ◽  
pp. 135
Author(s):  
Quang-Vi Ngo ◽  
Chai Yi ◽  
Trong-Thang Nguyen
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Wind Turbine ◽  
Pitch Angle ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Small Scale ◽  
Fuzzy Pid ◽  
Proportional Integral Derivative ◽  
Fuzzy Pid Controller

<p>This paper aims to design the pitch angle control based on proportional–integral–derivative (PID) controller combined with fuzzy logic for small-scale wind turbine systems. In this control system, the pitch angle is controlled by the PID controller with their parameter is tuned by the fuzzy logic controller. This control system can compensate for the nonlinear characteristic of the pitch angle and wind speed. A comparison between the fuzzy-PID-controller with the conventional PID controller is carried out. The effectiveness of the method is determined by the simulation results of a small wind turbine using a permanent magnet generator (PMSG).</p>

Simulation Analysis of Electro-Pneumatic Proportional Fuzzy-PID Control System Based on AMESim/Simulink

2014 ◽  
Vol 945-949 ◽  
pp. 2568-2572
Author(s):  
Si Yuan Wang ◽  
Guang Sheng Ren ◽  
Pan Nie
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Simulation Analysis ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Proportional Control ◽  
Fuzzy Pid Controller ◽  
Design Requirements ◽  
Set Up

The test rig for hydro-pneumatic converter used in straddle type monorail vehicles was researched, and its electro-pneumatic proportional control system was set up and simulated based on AMESim/Simulink. Compared fuzzy-PID (Proportion Integral Derivative) controller with PID controller through fuzzy logic tool box in Simulink, the results indicate that, this electro-pneumatic proportional control system can meet design requirements better, and fuzzy-PID controller has higher accuracy and stability than PID controller.

Research on Tyred Machinery Chassis Dynamometer

2013 ◽  
Vol 631-632 ◽  
pp. 1106-1110
Author(s):  
Wei Zhao ◽  
Qiang Wang ◽  
Sheng Li Song
Keyword(s):  
Control System ◽  
System Performance ◽  
Pid Controller ◽  
Fuzzy Pid ◽  
Chassis Dynamometer ◽  
Technical Parameters ◽  
The Road ◽  
Fuzzy Pid Controller ◽  
System A ◽  
Line Measure

In the tyred machinery chassis dynamometer control system, a fuzzy PID controller was used to adjust the exciting current of a DC dynamometer in order to change the resistance load torque, so the requirement of roller load for simulating the run resistance from the road surface was satisfied. A fuzzy PID arithmetic was designed to control the resistance loads, the system performance was improved by simulation. The software of the detection line measure-control system was designed in VB, the technical parameters of the machinery chassis could the automatically detected.

Design and Implementation of Fuzzy PID Controller for Brushless DC Motor Control System

2013 ◽  
Vol 432 ◽  
pp. 472-477
Author(s):  
Wei Fan ◽  
Tao Chen
Keyword(s):  
Control System ◽  
Digital Signal Processor ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Digital Signal ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Fuzzy Pid ◽  
Brushless Dc ◽  
Fuzzy Pid Controller

This paper presents a robust fuzzy proportional-integral-derivative (PID) controller for brushless DC motor (BLDCM) control system. The hardware circuit of the BLDCM control system is designed and implemented using a digital signal processor (DSP) TMS320LF2407A and a monolithic BLDCM controller MC33035 as the core. Furthermore, a fuzzy PID controller, which combines the advantages of good robustness of fuzzy controller and high precision of conventional PID controller, is employed in the hardware system, thereby yielding a digital, intelligent BLDCM control system. Experimental results have shown that the control system can run steadily and control accurately, and have convincingly demonstrated the usefulness of the proposed fuzzy PID controller in BLDCM control system.

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