Model Predictive Control System Design and Implementation Using ...

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Advances in Industrial Control
Other titles published in this series: Digital Controller Implementation and Fragility Robert S.H. Istepanian and James F. Whidborne (Eds.) Optimisation of Industrial Processes at Supervisory Level Doris Sáez, Aldo Cipriano and Andrzej W. Ordys Robust Control of Diesel Ship Propulsion Nikolaos Xiros Hydraulic Servo-systems Mohieddine Jelali and Andreas Kroll Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques Silvio Simani, Cesare Fantuzzi and Ron J. Patton Strategies for Feedback Linearisation Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati and Kevin Warwick Robust Autonomous Guidance Alberto Isidori, Lorenzo Marconi and Andrea Serrani Dynamic Modelling of Gas Turbines Gennady G. Kulikov and Haydn A. Thompson (Eds.) Control of Fuel Cell Power Systems Jay T. Pukrushpan, Anna G. Stefanopoulou and Huei Peng Fuzzy Logic, Identification and Predictive Control Jairo Espinosa, Joos Vandewalle and Vincent Wertz Optimal Real-time Control of Sewer Networks Magdalene Marinaki and Markos Papageorgiou Process Modelling for Control Benoît Codrons Computational Intelligence in Time Series Forecasting Ajoy K. Palit and Dobrivoje Popovic Modelling and Control of Mini-Flying Machines Pedro Castillo, Rogelio Lozano and Alejandro Dzul Ship Motion Control Tristan Perez Hard Disk Drive Servo Systems (2nd Ed.) Ben M. Chen, Tong H. Lee, Kemao Peng and Venkatakrishnan Venkataramanan Measurement, Control, and Communication Using IEEE 1588 John C. Eidson Piezoelectric Transducers for Vibration Control and Damping S.O. Reza Moheimani and Andrew J. Fleming Manufacturing Systems Control Design Stjepan Bogdan, Frank L. Lewis, Zdenko Kovačić and José Mireles Jr. Windup in Control Peter Hippe Nonlinear H 2 /H ∞ Constrained Feedback Control Murad Abu-Khalaf, Jie Huang and Frank L. Lewis Practical Grey-box Process Identification Torsten Bohlin Control of Traffic Systems in Buildings Sandor Markon, Hajime Kita, Hiroshi Kise and Thomas Bartz-Beielstein Wind Turbine Control Systems Fernando D. Bianchi, Hernán De Battista and Ricardo J. Mantz Advanced Fuzzy Logic Technologies in Industrial Applications Ying Bai, Hanqi Zhuang and Dali Wang (Eds.) Practical PID Control Antonio Visioli (continued after Index)
Page 4 and 5: Liuping Wang Model Predictive Contr
Page 6 and 7: Advances in Industrial Control Seri
Page 8 and 9: In memory of my parents
Page 10 and 11: x Series Editors’ Foreword Predic
Page 12 and 13: xii Preface Theory This book was or
Page 14 and 15: xiv Preface is indeed what I have t
Page 16 and 17: xvi Preface system is not necessari
Page 18 and 19: xviii Preface micro-controller for
Page 20 and 21: Contents List of Symbols and Abbrev
Page 22 and 23: Contents xxiii 3.8 Closed-form Solu
Page 24 and 25: Contents xxv 9.4 ExtensiontoMIMOSys
Page 26 and 27: xxviii List of Symbols and Abbrevia
Page 28 and 29: 1 Discrete-time MPC for Beginners 1
Page 30 and 31: 1.1 Introduction 3 following: the m
Page 32 and 33: 1.2 State-space Models with Embedde
Page 34 and 35: 1.3 Predictive Control within One O
Page 36 and 37: 1.3.2 Optimization 1.3 Predictive C
Page 42 and 43: 1.4 Receding Horizon Control 15 1.4
Page 44 and 45: (Φ T Φ + ¯R) −1 Φ T F. 1.4 Re
Page 46 and 47: omega=10; numc=omega^2; denc=[1 0.1
Page 48 and 49: 1.4 Receding Horizon Control 21 r=o
Page 50 and 51: 1.5 Predictive Control of MIMO Syst
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if A −1 11 and A−1 22 exist, th
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1.6 State Estimation 27 ⎡ ⎤ ⎡
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1.6 State Estimation 29 and the sec
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1.6 State Estimation 31 1 0 x1 xhat
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1.6 State Estimation 33 1.6.3 Kalma
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1.7 State Estimate Predictive Contr
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1.8 Summary 37 ⎡ where A = ⎣ 11
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1.8 Summary 39 (Garcia et al., 1989
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1.8 Summary 41 1.6. Time delay in a
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2 Discrete-time MPC with Constraint
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2.2 Motivational Examples 45 Output
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2.3 Formulation of Constrained Cont
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2.4 Numerical Solutions Using Quadr
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2.5 Predictive Control with Constra
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2.6 Summary 81 y u Delta u 1 0.5 0
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2.6 Summary 83 Problems 2.1. Assume
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3 Discrete-time MPC Using Laguerre
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3.2 Laguerre Functions and DMPC 87
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3.3 Use of Laguerre Functions in DM
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3.4 Extension to MIMO Systems 107 w
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J = η T Eη +2η T Hx(k i ), 3.5 M
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3.5 MATLAB Tutorial Notes 111 The p
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3.5 MATLAB Tutorial Notes 113 d44=1
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3.5.2 Predictive Control System Sim
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3.5 MATLAB Tutorial Notes 117 Outpu
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3.6 Constrained Control Using Lague
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3.7 Stability Analysis 127 Delta u
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3.7 Stability Analysis 129 and x(k
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3.8 Closed-form Solution of Constra
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3.9 Summary 143 3.9 Summary This ch
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3.9 Summary 145 transfer function (
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3.9 Summary 147 J = η T Ωη +2Ψx
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4 Discrete-time MPC with Prescribed
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4.2 Finite Prediction Horizon: Re-v
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4.3 Use of Exponential Data Weighti
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4.4 Asymptotic Closed-loop Stabilit
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Solution. 4.4 Asymptotic Closed-loo
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4.4 Asymptotic Closed-loop Stabilit
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4.5 Discrete-time MPC with Prescrib
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4.5 Discrete-time MPC with Prescrib
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Â T γ [P ∞ − P ∞ ˆB γ 4.5
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4.6 Tuning Parameters for Closed-lo
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4.7 Exponentially Weighted Constrai
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4.7 Exponentially Weighted Constrai
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4.8 Additional Benefit 183 10 8 6 R
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4.8 Additional Benefit 185 Delta u
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4.9 Summary 187 infinite horizon ca
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4.9 Summary 189 4.2. Continue from
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4.9 Summary 191 Design a predictive
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194 5 Continuous-time Orthonormal B
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6 Continuous-time MPC 6.1 Introduct
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6.2 Model Structures for CMPC Desig
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6.3 Model Predictive Control Using
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6.4 Optimal Control Strategy 225 wh
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6.5 Receding Horizon Control 227 Co
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6.5 Receding Horizon Control 229
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6.5 Receding Horizon Control 231 fo
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6.5 Receding Horizon Control 233 Lz
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6.6 Implementation of the Control L
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6.8 Summary 245 orthonormal propert
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6.8 Summary 247 Step response 1.2 1
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7 Continuous-time MPC with Constrai
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7.2 Formulation of the Constraints
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7.3 Numerical Solutions for the Con
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7.4 Real-time Implementation of Con
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7.4 Real-time Implementation of Con
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7.5 Summary 267 sented in Gawthrop
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7.5 Summary 269 2. Design a continu
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272 8 Continuous-time MPC with Pres
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9 Classical MPC Systems in State-sp
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9.2 Generalized Predictive Control
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9.3 Alternative Formulation to GPC
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C = [ 0000001 ] . With receding hor
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9.4 Extension to MIMO Systems 313 3
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9.4 Extension to MIMO Systems 315 E
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9.4 Extension to MIMO Systems 317 1
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9.4 Extension to MIMO Systems 319 C
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and the filtered disturbance as 9.5
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9.6 Case Studies for Continuous-tim
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9.6 Case Studies for Continuous-tim
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9.7 Predictive Control Using Impuls
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9.8 Summary 329 C l = [ ] c 1 c 2 c
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9.8 Summary 331 2. Design a predict
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10 Implementation of Predictive Con
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10.2 Predictive Control of DC Motor
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10.3 Implementation of Predictive C
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10.4 Control of Magnetic Bearing Sy
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10.4 Control of Magnetic Bearing Sy
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10.5 Continuous-time Predictive Con
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10.6 Summary 365 while the SME outp
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References 1. F. Allgower, T. A. Ba
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References 369 38. D. G. Luenberger
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References 371 82. D.A. Wismer and
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374 Index exponentially decreasing
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