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Predictive Functional Control Principles and Industrial Applications

:	Jacques Richalet, Donal O'Donovan
:	Predictive Functional Control Principles and Industrial Applications
:	Springer-Verlag
:	9781848824935
:	Advances in Industrial Control
:	1
:	CHF 85.90
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	224
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

first industrial application of MPC was in 1973. A key motivation was to provide better performance than could be obtained with the widely-used PID controller whilst making it easy to replace the PID controller unit or module with his new algorithm. It was the advent of digital control technology and the use of software control algorithms that made this replacement easier and more acceptable to process engineers. A decade of industrial practice with PFC was reported in the archival literature by Jacques Richalet et al. in 1978 in an important seminal Automatica paper. Around this time, Cutler and Ramaker published the dynamic matrix control algorithm that also used knowledge of future reference signals to determine a sequence of control signal adjustment. Thus, the theoretical and practical development of predictive control methods was underway and subsequent developments included those of generalized predictive control, and the whole armoury of MPC methods. Jacques Richalet's approach to PFC was to seek an algorithm that was:• easy to understand;• easy to install;• easy to tune and optimise. He sought a new modular control algorithm that could be readily used by the control-technician engineer or the control-instrument engineer. It goes without saying that this objective also forms a good market strategy.

Jaques Richalet was born in Versailles, France, in 1936.

He studied aeronautical engineering at ENSAE in Paris and graduated in 1960. He then went to Berkeley, USA, where he obtained his MSc degree under the guidance of Prof. Zadeh. Back in Paris he worked in the field of applied mathematics and received his PhD in 1965.

His interest in model-based predictive control started as early as 1968. In the same year he founded the process engineering consulting company ADERSA with a major breakthrough being the first commissioned application of model based predictive control to a binary distillation column in 1973.

Since then he has been active in the areas of process identification, modelling and diagnosis methods such as predictive maintenance. Applications range from petrochemical and food industry to faster systems as encountered in the automotive and defense sector.

He was a manager of ADERSA till 2001 and is still working as a consultant for modelling and predictive control. He now lives in Versailles in France.

In his academic career he published more than fifty articles as well as three books on identification and predictive control. He has been president of the National Committee of Automatic Control and chairman of EEC Interest Group 'CIDIC'. For his achievements he was awarded the status as Chevalier de l'Ordre National du Merite and many researchers would probably agree to his being called 'the grandfather of predictive control'. He received the Nordic Process Control Award in 2007. He is now retired.

	Series Editors Foreword	8
	Foreword	10
	Preface	11
	Intended Audience	11
	Reading Guide	12
	Acknowledgments	13
	Contents	15
	Abbreviations and Symbols	20
	1 Why Predictive Control?	22
	1.1 You would not drive your car using PID control	22
	1.2 Historical Context	23
	1.3 Breaking with the PID Tradition	24
	1.4 Impact on Industry	26
	1.5 Objective	27
	1.6 Predictive Control Block Diagram	29
	1.7 Summary	30
	2 Internal Model	31
	2.1 Why Is Prediction Necessary?	31
	2.2 Model Types	32
	2.3 Decomposition of Unstable or Non-asymptotically Stable Systems	35
	2.4 Prediction	38
	2.5 Summary Summary Summary	40
	3 Reference Trajectory	42
	3.1 Introduction	42
	3.2 Reference Trajectory	43
	3.3 Pure Time Delay	45
	3.4 Summary	48
	4 Control Computation	49
	4.1 Elementary Calculation	49
	4.2 No Integrator?	52
	4.3 Basis Functions Functions Functions	54
	4.4 Extension	59
	4.5 Implicit Regulator Calculation	59
	4.6 Control of an Integrator Process	61
	4.7 Feedforward Compensation	63
	4.8 Extension: MV Smoothing	72
	4.9 Convolution Representation	74
	4.10 Extension to Higher-order System Models	76
	4.11 Controller Initialisation	84
	4.12 Summary	86
	5 Tuning	88
	5.1 Regulator Objectives	88
	5.2 Accuracy	89
	5.3 Dynamics	90
	5.4 Robustness	94
	5.5 Choice of Tuning Parameters	96
	5.6 Gain Margin as a Function of CLTR (First-order System)	101
	5.7 Tuning	102
	5.8 The Tuner s Rule	106
	5.9 Practical Guidelines	108
	5.10 Summary	109
	6 Constraints	110
	6.1 Benefit	110
	6.2 MV Constraints	111
	6.3 Internal Variable Constraints	113
	6.4 Constraint Transfer Back Calculation	117
	6.5 Summary	119
	7 Industrial Implementation	120
	7.1 Implementation	120
	7.2 Zone Control	121
	7.3 Cascade Control	124
	7.4 Transparent Control	125
	7.5 Shared Multi-MV Control	127
	7.6 Estimator	134
	7.7 Non-linear Control	139
	7.8 Scenario Method	143
	7.9 2MV/2CV Control	144
	7.10 Summary	150
	8 Parametric Control	151
	8.1 Parametric Instability	151
	8.2 Heat Exchanger	152
	8.3 Constraint Transfer in Parametric Control	157
	8.4 Evaluation	159
	8.5 Summary	160
	9 Unstable Poles and Zeros	161
	9.1 Complexity	161
	9.2 Stable Pole and Stable Zero	163
	9.3 Unstable Zero and a Stable Pole	164
	9.4 Control of an Unstable, Minimum Phase Process	165
	9.5 Control of an Unstable, Non-minimum Phase Process	166
	9.6 Summary	172
	10 Industrial Examples	173
	10.1 Industrial Applications	173
	10.2 Heat Exchanger	174
	10.3 Institut de Régulation d Arles Exchanger	184
	10.4 ARCELOR	193
	10.5 EVONIK.DEGUSSA	213
	10.6 Summary	216
	11 Conclusions	217
	11.1 Characteristics of PFC Control	218
	11.2 Limits of PFC Control	219
	11.3 Final Remark	221
	Appendix A	222
	A.1 First-Order Process (K,T,D) in MATLAB	222
	Appendix B	231
	B.1 Calculation of Heat-Transfer Coefficient for Water	231
	References	233
	Index	234