A new type of computational verb gain-scheduling PID controller

2010 ◽  
Author(s):  
Yi Guo ◽  
Tao Yang
Keyword(s):  
Pid Controller ◽  
Gain Scheduling ◽  
New Type

scholarly journals Gain Scheduling of PID Controller Based on Fuzzy Systems

2016 ◽  
Vol 57 ◽  
pp. 01008 ◽  
Author(s):  
Sandeep Singh ◽  
Mandeep Kaur
Keyword(s):  
Fuzzy Systems ◽  
Pid Controller ◽  
Gain Scheduling

Adaptive PID Controller Based on Single Neuron

2012 ◽  
Vol 466-467 ◽  
pp. 981-985 ◽  
Author(s):  
Xin Yun Qiu ◽  
Yuan Gao
Keyword(s):  
Pid Controller ◽  
Control Algorithm ◽  
Single Neuron ◽  
Variable Parameter ◽  
Fast Response ◽  
Control Law ◽  
Parameters Tuning ◽  
Application Condition ◽  
Simulation And Experiment ◽  
New Type

An adaptive PID controller based on single neuron is proposed. The properties, control algorithm, parameters tuning, the control law and the application condition of the controller are studied in the paper. To satisfy the properties of the requirements of the control system in an electromotor group, such as a broad dynamic changing range, a fast response, a little overshoot and time-variable parameter, a new-type self-optimizing PID controller based on artificial neural networks is proposed and studied. It is verified that the controller has few adjustable parameters and excellent robust performance. The results of simulation and experiment prove that the controller is superior to the traditional PID controller.

Precision Water Distribution Control of New Type Sewage Distributor

2012 ◽  
Vol 155-156 ◽  
pp. 1015-1019
Author(s):  
Yi Shu Hao ◽  
Kai Shun Ji ◽  
Zong Yue Liu
Keyword(s):  
Pid Controller ◽  
Water Distribution ◽  
Sewage Treatment ◽  
Flow Distribution ◽  
Uniform Flow ◽  
Controlled System ◽  
Uniform Flow Distribution ◽  
Stepper Motors ◽  
New Type ◽  
Novel Method

This paper focuses on the issues of uniform flow distribution control for sewage treatment devices. A novel method for uniform flow distribution was proposed, in which a new type sewage distributor employing two phrase stepper motors is integrated to replace the conventional one. The model of permanent magnet stepper motor is mainly discussed. A proportional-integral-derivative (PID) controller is designed for this new type distributor. The performance of general controlled system is simulated, compared by tuning the parameters. And the control system is evaluated in practical use.

SPEED CONTROL OF BLDC MOTOR WITH SEAMLESS SPEED REVERSAL CAPABILITY USING MODIFIED FUZZY GAIN SCHEDULING

2018 ◽  
Vol 80 (2) ◽  
Author(s):  
Satishrao Pothorajoo ◽  
Hamdan Daniyal
Keyword(s):  
Pid Controller ◽  
Speed Control ◽  
Gain Scheduling ◽  
Test Case ◽  
Test Cases ◽  
Nonlinear Loads ◽  
Speed Test ◽  
Fuzzy Pid Controller ◽  
Speed Controller ◽  
Self Tuning

Brushless Direct Current (BLDC) motors have gained popularity in recent years due to their high-power density. Many type of speed controller techniques have been developed and Proportional Integral Derivative (PID) controller has been the most widely used. However, PID’s performance deteriorates during nonlinear loads conditions. Over the past five years, controllers have been developed to overcome this limitations in BLDC speed control, however the solutions are focusing on forward motoring only. In this paper, a speed controller for BLDC with seamless speed reversal using Modified Fuzzy Gain Scheduling is proposed. The proposed controller regulates the speed using Fuzzy Gain Scheduling 49 base rules. The controller was tested for six test cases and compared to PID and Self-Tuning Fuzzy PID controller. It is found out the proposed controller yields lowest steady state error, ess of 0.025 % during step-changing speed test case. Overall, Modified Fuzzy Gain Scheduling BLDC speed controller outperforms the other two similar controllers in variable speed conditions. The controller has potential to be used as bidirectional drive in highly dynamic load conditions.

A Study of PID Control System Based on BP Neural Network

2011 ◽  
Vol 328-330 ◽  
pp. 1908-1911
Author(s):  
Wei Liu ◽  
Jian Jun Cai ◽  
Xi Pin Fan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Steepest Descent ◽  
The Neural Network ◽  
On Line ◽  
New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

Design of Multi-Mode PID Controller and Application in Time-Delay Process

2014 ◽  
Vol 511-512 ◽  
pp. 637-642
Author(s):  
Yu Mei Chen ◽  
Fei Tan ◽  
Tao Fan
Keyword(s):  
Time Delay ◽  
Transient Response ◽  
Intelligent Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
Simple Structure ◽  
Excitation Control ◽  
New Type ◽  
Simulation Results ◽  
Multi Mode

Through brief analysis of characteristics of conventional control, a new type of multi-mode intelligent control algorithm based on error information is put forward. The algorithm consists of proportional acceleration control for rapidity of transient response, differential deceleration control for stationarity of transient response and steady state excitation control for accuracy of steady response. The control algorithm is applied to time-delay process, compared with other algorithms. Simulation results show its good performance with MATLAB language. The algorithm has simple structure, good generality and easy adjustment.

Optimum Gain-Scheduling PID Controllers for Gas Turbine Engines Based on NARMAX and Neural Network Models

2003 ◽  
Author(s):  
Junxia Mu ◽  
David Rees ◽  
Neophytos Chiras
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Pid Controller ◽  
Network Models ◽  
Gain Scheduling ◽  
Pid Controllers ◽  
Neural Network Models ◽  
Turbine Engine ◽  
Dynamic Relationship ◽  
Fuel Flow

This paper presents PID controller designs based on NARMAX and feedforward neural network models of a Spey gas turbine engine. Both models represent the dynamic relationship between the fuel flow and shaft speed. Due to the engine non-linearity, a single set of PID controller parameters is not sufficient to control the gas turbine throughout the operating range. Gain-scheduling PID controllers are therefore used in order to obtain optimum control. A comparison between the controller designs based on the two model representations is also made.

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