Setpoint weighted PID controller tuning for unstable system using heuristic algorithm

Abstract

Most of the real time chemical process loops are unstable in nature and designing a suitable controller for such systems are difficult than open loop stable processes. In this work, an attempt is made with a two degree of freedom setpoint weighted PID controller tuning procedure for a class of unstable systems using the recent heuristic algorithms such as Particle Swarm Optimization and Bacterial Foraging Optimization. The problem considered in this study is to aptly tune the controller in order to enhance the overall closed loop performance. A novel objective function proposed in this study is used to monitor the heuristic algorithms in order to get the optimal controller parameters like K p , K i , K d , and α with minimized iteration number. The proposed method is validated with a simulation study and this helps to accomplish enhanced system performance such as smooth reference tracking, satisfactory disturbance rejection, and error minimization for a class of unstable systems.

Implementation of unconventional modified PID controllers in PLC

“…Autorzy prac [2,3] przedstawili algorytm ważonego regulatora PID. Dodatkowo pokazano heurystyczną metodykę doboru nastaw, z wykorzystaniem optymalizacji [3]. Sprawdzono, czy możliwe jest zbudowanie adaptacyjnego regulatora PI z częścią predykcyjną [4].…”

“…Już w 1995 r. zaprezentowano rozszerzone algorytmy regulatora PID [1]. Autorzy prac [2,3] przedstawili algorytm ważonego regulatora PID. Dodatkowo pokazano heurystyczną metodykę doboru nastaw, z wykorzystaniem optymalizacji [3].…”

Design of PID Controller for SISO and TITO Systems – A Study with Heuristic Algorithms

“…It is a population based evolutionary computation technique, attempts to mimic the natural process of group communication of individual knowledge, to achieve some optimum property. More details regarding the PSO can be found in [2,3].…”

“…In chemical industry, most of the important processing units are highly non-linear in nature and for economical and/or safety reasons, these process loops to be operated in safe regions with the help of a PID controller [1][2][3]. Based on its operating region and its non-linearity, these loops can be modelled as SISO and MIMO systems.…”

Optimized Controller Structured to Solve Aircraft Pitch Control Problem

“…Literature evident that, even though there exists a number of heuristic methods, algorithms such as Pa rticle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), and Firefly Algorithm (FA) are wide ly adapted by most of the researchers to find best possible solutions for various controller design problems [3][4][5].…”