Advances in Industrial Control

Principles and Industrial Applications

By: Karl E. Ã?strÃ¶m (Foreword by), Jacques Richalet, Donal O'Donovan

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Hardcover | 25 May 2009

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248 Pages

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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: â¢ easy to understand; â¢ easy to install; â¢ 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.

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Published: 2nd January 2013

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Abbreviations and Symbols	p. xxi
Why Predictive Control?	p. 1
"You would not drive your car using PID control"	p. 1
Historical Context	p. 2
Breaking with the PID Tradition	p. 3
Impact on Industry	p. 5
The Petrochemical Industry	p. 6
The Defence Industry	p. 6
Objective	p. 6
Formulation	p. 7
Implementation	p. 7
Process	p. 8
Predictive Control Block Diagram	p. 8
Summary	p. 9
Internal Model	p. 11
Why Is Prediction Necessary?	p. 11
Model Types	p. 12
Realigned Model	p. 13
Independent Model	p. 13
Decomposition of Unstable or Non-asymptotically Stable Systems	p. 15
Measurable Disturbance Compensation	p. 15
Decomposition	p. 16
Prediction	p. 18
The Free Solution S_L(t)	p. 18
The Forced Solution S_F(t)	p. 19
Summary	p. 20
Reference Trajectory	p. 23
Introduction	p. 23
Reference Trajectory	p. 24
Pure Time Delay	p. 26
Summary	p. 29
Control Computation	p. 31
Elementary Calculation	p. 31
How to Determine Future Model Outputs	p. 32
How to Structure the Future MVs	p. 32
No Integrator	p. 34
Basis Functions	p. 36
What MVs Are Required?	p. 36
Computational Requirements	p. 38
Polynomial Set-point	p. 38
Extension	p. 41
Implicit Regulator Calculation	p. 41
Control of an Integrator Process	p. 43
Feedforward Compensation	p. 45
Process Without Time Delay	p. 46
Process with Time Delay	p. 49
Extension	p. 50
Extension: MV Smoothing	p. 54
Convolution Representation	p. 56
Extension to Higher-order System Models	p. 58
Real Poles	p. 59
Complex Poles	p. 59
Control of a Pure Oscillator	p. 62
First-order Stable Process with a Stable or Unstable Zero	p. 64
Controller Initialisation	p. 66
PFC Controller Permanently Installed	p. 66
PFC Commissioning Phase	p. 68
Summary	p. 68
Tuning	p. 71
Regulator Objectives	p. 71
Accuracy	p. 72
Dynamics	p. 73
Time Response	p. 73
Frequency Response	p. 75
Robustness	p. 77
Choice of Tuning Parameters	p. 79
Accuracy	p. 79
Dynamics	p. 80
Robustness	p. 80
Gain Margin as a Function of CLTR (First-order System)	p. 84
Tuning	p. 85
Gain Margin/Delay Margin	p. 86
Sensitivity Function ("Hill Curve")	p. 88
The Tuner's Rule	p. 89
Practical Guidelines	p. 91
Summary	p. 92
Constraints	p. 93
Benefit	p. 93
MV Constraints	p. 94
Impact of Constraints on the CL'TR	p. 96
Internal Variable Constraints	p. 96
Constraint Transfer-Back Calculation	p. 100
Summary	p. 102
Industrial Implementation	p. 103
Implementation	p. 103
Zone Control	p. 104
Cascade Control	p. 107
Transparent Control	p. 108
Shared Multi-MV Control	p. 110
Coupled Actuators	p. 111
Split-range Control	p. 114
Estimator	p. 117
State and Structural Disturbances	p. 118
Feedforward Variable	p. 120
Calibration	p. 121
Non-linear Control	p. 122
Non-linearities of the MV or CV	p. 122
Non-linear Stationary Feedback	p. 123
Scenario Method	p. 126
2MV/2CV Control	p. 127
Tire Problem Being Addressed	p. 127
Control Computation	p. 128
Constraints on the MVs	p. 129
Tuning	p. 131
Summary	p. 133
Parametric Control	p. 135
Parametric Instability	p. 135
Heat Exchanger	p. 136
Constraint Transfer in Parametric Control	p. 141
Evaluation	p. 143
Summary	p. 144
Unstable Poles and Zeros	p. 145
Complexity	p. 145
Stable Pole and Stable Zero	p. 147
Unstable Zero and a Stable Pole	p. 148
Control of an Unstable, Minimum Phase Process	p. 149
Control of an Unstable, Non-minimum Phase Process	p. 150
Stability	p. 152
Robustness	p. 353
Tuning	p. 155
Summary	p. 156
Industrial Examples	p. 157
Industrial Applications	p. 157
Heat Exchanger	p. 158
Problem Description	p. 158
Convexity Theorem	p. 159
Fluid/Mass Exchange	p. 160
Counter-current Exchanger	p. 166
Implementation of a First-principles Model Controller	p. 167
Institut de Régulation d' Arles Exchanger	p. 168
Heat-exchanger Model	p. 168
Control	p. 170
Results	p. 172
Arcelor	p. 177
Continuous Casting	p. 178
GV1/2 Steam Generators	p. 184
Evontk.Degussa	p. 197
Summary	p. 200
Conclusions	p. 201
Characteristics of PFC Control	p. 202
Model	p. 202
Reference Trajectory	p. 202
Regulator	p. 202
Basis Functions	p. 202
Time and Frequency Domains	p. 203
Limits of PFC Control	p. 203
Multiple-Input, Multiple-Output Extension	p. 203
Constraints	p. 203
Effort	p. 203
Risk	p. 204
Final Remark	p. 205
	p. 207
First-Order Process (K, T, D) in MATLAB“, C++ and VB	p. 207
Matlab	p. 207
C++	p. 209
In Visual Basic	p. 211
First-order Integrator Process	p. 214
	p. 217
Calculation of Heat-Transfer Coefficient for Water	p. 217
Time-constant Calculation	p. 218
References	p. 219
Index	p. 221
Table of Contents provided by Ingram. All Rights Reserved.

Advances in Industrial Control

Principles and Industrial Applications

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