Advanced Control of Industrial Processes

Advanced Control of Industrial Processes Structures and Algorithms Springer . Contents ... 3 Model-based Predictive Control 107 3.1 The Principle of P...

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Piotr Tatjewski

Advanced Control of Industrial Processes Structures and Algorithms

Springer

Contents

Notation

xvii

1

Multilayer Control Structure 1.1 Control System 1.2 Control Objectives 1.3 Control Layers 1.4 Process Modeling in a Multilayer Structure 1.5 Optimization Layer 1.6 Supervision, Diagnosis, Adaptation

1 1 2 4 9 24 29

2

Model-based Fuzzy Control 2.1 Takagi-Sugeno (TS) Type Fuzzy Systems 2.1.1 Fuzzy Sets and Linguistic Variables 2.1.2 Fuzzy Reasoning 2.1.3 Design of TS Fuzzy Models 2.1.4 TS System as a Fuzzy Neural Network 2.2 Discrete-time TS Fuzzy Control 2.2.1 Discrete TS Fuzzy State-feedback Controllers 2.2.2 Discrete TS Fuzzy Output-feedback Controllers 2.3 Continuous-time TS Fuzzy Control 2.3.1 Continuous TS Fuzzy State-feedback Controllers 2.3.2 Continuous TS Fuzzy Output-feedback Controllers . . . . 2.4 Feedforward Compensation, Automatic Tuning

33 35 35 39 46 48 55 58 71 83 84 95 103

3

Model-based Predictive Control 3.1 The Principle of Predictive Control 3.2 Dynamic Matrix Control (DMC) Algorithm 3.2.1 Output Predictions Using Step Response Models 3.2.2 Unconstrained Explicit DMC Algorithm 3.2.3 Constraining the Controller Output by Projection 3.2.4 DMC Algorithm in Numerical Version

107 107 118 118 123 135 139

xvi

Contents 3.3

3.4

3.5

3.6

4

3.2.5 Model Uncertainty, Disturbances 142 Generalized Predictive Control (GPC) Algorithm 149 3.3.1 GPC Algorithm for a SISO Process 151 3.3.2 GPC with Constant Output Disturbance Prediction . . . 166 3.3.3 GPC Algorithm for a MIMO Process 168 3.3.4 GPC Algorithm in Numerical Version 170 MPC with State-space Process Model 176 3.4.1 Algorithms with Measured State 177 3.4.2 Algorithms with Estimated State 186 3.4.3 Explicit Piecewise-affine MPCS Constrained Controller. 194 Nonlinear Predictive Control Algorithms 197 3.5.1 Structures of Nonlinear MPC Algorithms 197 3.5.2 MPC-NO (MPC with Nonlinear Optimization) 198 3.5.3 MPC-NSL (MPC Nonlinear with Successive Linearization) 200 3.5.4 MPC-NPL (MPC with Nonlinear Prediction and Linearization) 202 3.5.5 MPC Algorithms Using Artificial Neural Networks 211 3.5.6 Comparative Simulation Studies 218 3.5.7 Fuzzy MPC (FMPC) Numerical Algorithms 228 3.5.8 Fuzzy MPC (FMPC) Explicit Unconstrained Algorithms 242 Stability, Constraint Handling, Parameter Tuning 249 3.6.1 Stability of MPC Algorithms 249 3.6.2 Feasibility of Constraint Sets, Parameter Tuning 262

Set-point Optimization 4.1 Steady-state Optimization in Multilayer Process Control Structure 4.2 Steady-state Optimization for Model Predictive Control 4.2.1 MPC Steady-state Target Optimization 4.2.2 Integrated Approach to MPC and Steady-state Optimization 4.2.3 Adaptive MPC Integrated with Steady-state Optimization 4.2.4 Comparative Example Results 4.3 Measurement-based Iterative Set-point Optimization under Uncertainty 4.3.1 Integrated System Optimization and Parameter Estimation (ISOPE) 4.3.2 ISOPE for Problems with Output Constraints

273 273 277 280 287 289 292 300 301 314

References

317

Index

327