PID Control, Implementation and Tuning doc

   Edited by  PID Control, Implementation and Tuning Edited by Tamer Mansour Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Ivana Lorkovic Technical Editor Goran Bajac Cover Designer Martina Sirotic Image Copyright AntonSokolov, 2010. Used under license from Shutterstock.com First published March, 2011 Printed in India A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org PID Control, Implementation and Tuning, Edited by Tamer Mansour p. cm. ISBN 978-953-307-166-4 free online editions of InTech Books and Journals can be found at www.intechopen.com Part 1 Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Part 2 Chapter 7 Preface VII PID Implementation 1 Multivariable PID control of an Activated Sludge Wastewater Treatment Process 3 Norhaliza Abdul Wahab, Reza Katebi and Jonas Balderud Stable Visual PID Control of Redundant Planar Parallel Robots 27 Miguel A. Trujano, Rubén Garrido and Alberto Soria Pid Controller with Roll Moment Rejection for Pneumatically Actuated Active Roll Control (Arc) Suspension System 51 Khisbullah Hudha, Fauzi Ahmad, Zulkiffli Abd. Kadir and Hishamuddin Jamaluddin Application of Improved PID Controller in Motor Drive System 91 Song Shoujun and Liu Weiguo PID control with gravity compensation for hydraulic 6-DOF parallel manipulator 109 Chifu Yang, Junwei Han, O.Ogbobe Peter and Qitao Huang Sampled-Data PID Control and Anti-aliasing Filters 127 Marian J. Blachuta and Rafal T. Grygiel PID Tuning 143 Multi-Loop PID Control Design by Data-Driven Loop-Shaping Method 145 Masami Saeki and Ryoyu Kishi Contents Contents VI Neural Network Based Tuning Algorithm for MPID Control 163 Tamer Mansour, Atsushi Konno and Masaru Uchiyama Adaptive PID Control for Asymptotic Tracking Problem of MIMO Systems 187 Kenichi Tamura and Hiromitsu Ohmori Pre-compensation for a Hybrid Fuzzy PID Control of a Proportional Hydraulic System 201 Pornjit Pratumsuwan and Chaiyapon Thongchaisuratkrul A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network 217 Ho Pham Huy ANH and Nguyen Thanh Nam Chapter 8 Chapter 9 Chapter 10 Chapter 11 The Proportional, Integral and Derivative –PID– controller is the most widely used controller in industrial applications. Since its rst appearance in the late nineteen cen- tury, it had aracted researchers from all over the world because of its simplicity and the ability to provide an excellent control performance. The PID controller now repre- sents more than ninety percent of the controllers used in the market. This book is a result of contributions and inspirations from many researchers world- wide in the eld of control engineering. The book consists of two parts; the rst is related to the implementation of PID control in various applications whilst the second part concentrates on the tuning of PID control to get best performance. Firstly, I wish to thank the authors, who contributed to the production of this book. Also, I wish to convey deepest gratitude to reviewers who devoted their time to review the manuscripts and selected the best of them. We hope that this book can give aid to new research in the eld of PID control, in addition to stimulating research in the area of PID control for beer utilization in the industrial world. Tamer Mansour Preface PID Implementation Part 1 PID Implementation [...]... MPID These are about 9.8%, 11.7% and 11.9%, for M2, M3 and M4, respectively The average effluent ammonia (NH4Neff) was reduced by 10.3% and 4.6%, for M3 and M4, respectively M2 gave slightly higher average effluent ammonia but still below the discharge limit (4mg/l) Better total nitrogen removal is achieved using M4 for both ammonia and nitrate in the effluent 24 PID Control, Implementation and Tuning. .. large s H n (s)  (22) The tuning parameters,  and  can be used to tune the proportional and integral gains 4.1.3 Maciejowski method M3 extends M2 to non-zero frequencies and hence the controller gains are linearly related to the inverse of the plant dynamics at a particular design frequency, wb, i.e 14 PID Control, Implementation and Tuning 1 K c   G 1 ( jwb ), and K i   G 1 ( jwb ) The... relevant low and intermediate 12 PID Control, Implementation and Tuning frequency parts for both conditions in this case and it can be conclude that the plots demonstrate the interactions occur mainly at frequencies about a decade below the open loop bandwidth The low frequency decoupling is therefore most likely to decentralize the control system and to minimise the effect of interactions 4 MPID Control... multiloop control tuning It shows good performance in both loops in terms of closed loop bandwidth and can suppress noise better 22 PID Control, Implementation and Tuning (a) (b) (c) Fig 10 Performance robustness analysis of Case 2 - sensitivity- a) Penttinen method; b) Maciejowski method; c) Proposed new method Fig 11 shows the plots of input disturbance,   I  GK  G for both cases of 1 and 2 In this... following steady state Lyapunov equation: AcT P  PAc  Q  K T RK  0 (33) 16 PID Control, Implementation and Tuning where Ac  A  BK Thus, for each MPID control scheme, the controller parameters,  is selected such that the matrix norm of P is minimised, i.e.: min P , (34)  where  is given in Table 3 and Table 4 for both Cases 1 and 2, respectively Constant M2 Rain  68.11 0.238 6.30  Ki  18.7 64.95... identification which later used for MPID controller design Case 1 The controller maintains the DO levels in the last three aerobic tanks as seen in Fig 1, by manipulation of oxygen transfer coefficients (KLa) Models are developed at three different operating conditions, i.e constant influent flow, dry influent flow and rain influent flow conditions 6 PID Control, Implementation and Tuning Case 2 In this case,... of the activated sludge process In both cases, the 8 PID Control, Implementation and Tuning simulation started at zero initial conditions The performance quality of the models are performed by measuring percentage Variance Accounted For (VAF) as follows: ˆ  var( y  y)  VAF (%)  1   *100 var( y)   (7) ˆ where y and y are the measured outputs and predicted outputs, respectively The bestˆ identified... Matlab numerical optimisation function This approach is justified when the process interaction is strong and the trial -and- error tuning approach would be time consuming The optimal tuning matrices for all MPID controllers for Cases of 1 and 2 at various operating points are evaluated The input and output weights in the cost function may be tuned in such a way that satisfactory closed loop performance,... constants for DO and SNO are of the order of minutes (DO) and hours (SNO), respectively The aim of the controller in Case 1 is to maintain the DO levels in the last three aerobic tanks at DO3=1.5mg/l, DO4=3mg/l and DO5=2mg/l In Case 2, the set points for DO and the nitrate were set at 2mg/l and 1mg/l, respectively Notice that, for simplification, each method of tuning is denoted as M1, M2, M3 and M4 methods... tracking and disturbance rejection performance M2 tends to make the system unstable as the controller gain is 18 PID Control, Implementation and Tuning increased M3 has better performance than M1 or M2, but it has slightly bigger overshoot than M4 Although the performance of M3 is satisfactory in some outputs, it uses the more time-consuming “sequential” identification procedure for obtaining the tuning . Junwei Han, O.Ogbobe Peter and Qitao Huang Sampled-Data PID Control and Anti-aliasing Filters 127 Marian J. Blachuta and Rafal T. Grygiel PID Tuning 143 Multi-Loop PID Control Design by Data-Driven. of interest happens for the relevant low and intermediate PID Control, Implementation and Tuning1 2 frequency parts for both conditions in this case and it can be conclude that the plots demonstrate. world. Tamer Mansour Preface PID Implementation Part 1 PID Implementation Multivariable PID control of an Activated Sludge Wastewater Treatment Process 3 Multivariable PID control of an Activated