Velocity form PID controller for control and modeling of nonlinear processes using NN based approach

2011 ◽  
Author(s):  
Satya Sheel ◽  
Gunjan Verma
Keyword(s):  
Pid Controller ◽  
Nonlinear Processes ◽  
Velocity Form

Application of Neural Networks and PID in on Line of Real-Time Temperature Control System

2011 ◽  
Vol 110-116 ◽  
pp. 5009-5014
Author(s):  
Suleiman A. Elfandi ◽  
Mahmoud A. Osman ◽  
Nasser Ali ◽  
Nuri Agab
Keyword(s):  
Real Time ◽  
Pid Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Temperature Control System ◽  
Controlled System ◽  
Data Acquisition Card ◽  
On Line ◽  
Neural Network Algorithm ◽  
Velocity Form

The Heat Process Trainer PT326 (Feedback UK) model is obtained by using two different techniques one by the Ziegler-Nichols approximating method and the other with the system identification method. The Data acquisition card EC641 I/O PT326 is developed and tested. it can be used as an interface between PC and any similar controlled system. The “velocity” form of the PID controller algorithm is implemented in real time where this algorithm is an ideal of solving the bump less transfer mechanism between manual and automatic control operation and the implementation of anti-reset windup algorithm. The method of back propagation neural network algorithm online gives very good results with neither steady state error nor overshoots.

PSO Tuned PID-Based Model Reference Adaptive Controller for Coupled Tank System

2014 ◽  
Vol 626 ◽  
pp. 167-171 ◽  
Author(s):  
D. Jeraldin Auxillia ◽  
T. Anitta
Keyword(s):  
Pid Controller ◽  
Environmental Changes ◽  
Pso Algorithm ◽  
Adaptive Controller ◽  
Nonlinear Processes ◽  
Process Industries ◽  
Model Reference ◽  
Effective Performance ◽  
Tank System ◽  
Model Reference Adaptive

In process industries control of nonlinear processes like level process is a common problem. For these nonlinear processes, to have a good performance even in the presence of environmental changes, the controller must have the ability to adopt changes in plant dynamics. So a Model Reference Adaptive Control (MRAC) based on PID controller is designed. Main objective of this work is to design PID-MRAC to include adaptiveness and to give good steady-state and transient performance for coupled tank system both with interaction and without interaction. A Particle Swarm Optimization (PSO) algorithm is used to fine tune the PID controller parameters. Simulations are done to show the effective performance of PID-MRAC compared to simple MRAC applied to couple tank system.

Design of a Data-Driven Multi-loop Self-Tuning PID Controller

2019 ◽  
Vol 139 (4) ◽  
pp. 356-363
Author(s):  
Yoichiro Ashida ◽  
Shin Wakitani ◽  
Toru Yamamoto
Keyword(s):  
Pid Controller ◽  
Data Driven ◽  
Self Tuning
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