A First Course in Predictive Control

Chapter 1: Introduction and the industrial need for predictive control

• • 1.1 Guidance for the lecturer/reader
• • 1.2 Motivation and introduction
• • 1.3 Classical control assumptions
• • 1.4 Examples of systems hard to control effectively with classical methods
• • 1.5 The potential value of prediction
• • 1.6 The main components of MPC
• • 1.7 MPC philosophy in summary
• • 1.8 MATLAB files from this chapter
• • 1.9 Reminder of book organization

Chapter 2: Prediction in model predictive control

• • 2.1 Introduction
• • 2.2 Guidance for the lecturer/reader
• • 2.3 General format of prediction modelling
• • 2.4 Prediction with state space models
• • 2.5 Prediction with transfer function models – matrix methods
• • 2.6 Using recursion to find prediction matrices for CARIMA models
• • 2.7 Prediction with independent models
• • 2.8 Prediction with FIR models
• • 2.9 Closed-loop prediction
• • 2.10 Summary of Chapter
• • 2.11 Summary of MATLAB code supporting prediction

Chapter 3: Predictive functional control

• • 3.1 Introduction
• • 3.2 Guidance for the lecturer/reader
• • 3.3 Basic concepts in PFC
• • 3.4 PFC with first order models
• • 3.5 PFC with higher order models
• • 3.6 Stability results for PFC
• • 3.7 PFC with ramp targets
• • 3.8 Chapter summary
• • 3.9 MATLAB code available for readers

Chapter 4: Predictive control – the basic algorithm

• • 4.1 Introduction
• • 4.2 Guidance for the lecturer/reader
• • 4.3 Summary of main results
• • 4.4 The GPC performance index
• • 4.5 GPC algorithm formulation for transfer function models
• • 4.6 GPC formulation for finite impulse response models and Dynamic Matrix Control
• • 4.7 Formulation of GPC with an independent prediction model
• • 4.8 GPC with a state space model
• • 4.9 Chapter summary and general comments on stability and tuning of GPC
• • 4.10 Summary of MATLAB code supporting GPC simulation

Chapter 5: Tuning GPC: good and bad choices of the horizons

• • 5.1 Introduction
• • 5.2 Guidance for the lecturer/reader
• • 5.3 Poor choices lead to poor behavior
• • 5.4 Concepts of well posed and ill posed optimizations
• • 5.5 Illustrative simulations to show impact of different parameter choices on GPC behavior
• • 5.6 Systematic guidance for "tuning" GPC
• • 5.7 MIMO examples
• • 5.8 Dealing with open-loop unstable systems
• • 5.9 Chapter summary: guidelines for tuning GPC
• • 5.10 Useful MATLAB code

Chapter 6: Dual mode MPC (OMPC and SOMPC) and stability guarantees

• • 6.1 Introduction
• • 6.2 Guidance for the lecturer
• • 6.3 Foundation of a well posed MPC algorithm
• • 6.4 Dual mode MPC – an overview
• • 6.5 Algebraic derivations for dual mode MPC
• • 6.6 Closed-loop paradigm implementations of OMPC
• • 6.7 Numerical illustrations of OMPC and SOMPC
• • 6.8 Motivation for SOMPC: Different choices for mode 2 of dual mode control
• • 6.9 Chapter summary
• • 6.10 MATLAB files in support of this chapter

Chapter 7: Constraint handling in GPC/finite horizon predictive control

• • 7.1 Introduction
• • 7.2 Guidance for the lecturer
• • 7.3 Introduction
• • 7.4 Description of typical constraints and linking to GPC
• • 7.5 Constrained GPC
• • 7.6 Understanding a quadratic programming (QP) optimization
• • 7.7 Chapter summary
• • 7.8 MATLAB code supporting constrained MPC simulation

Chapter 8: Constraint handling in dual mode predictive control

• • 8.1 Introduction
• • 8.2 Guidance for the lecturer/reader and introduction
• • 8.3 Background and assumptions
• • 8.4 Description of simple or finite horizon constraint handling approach for dual mode algorithms
• • 8.5 Concepts of redundancy, recursive feasibility, admissible sets and autonomous models
• • 8.6 The OMPC/SOMPC algorithm using an MCAS to represent constraint handling
• • 8.7 Numerical examples of the SOMPC/OMPMC approach with constraint handling
• • 8.8 Discussion on the impact of cost function and algorithm selection on feasibility
• • 8.9 Chapter summary
• • 8.10 Summary of MATLAB code supporting constrained OMPC simulation

Chapter 9: Conclusions

• • 9.1 Introduction
• • 9.2 Design choices
• • 9.3 Summary

Chapter Appendix A: Tutorial and exam questions and case studies

• • A.1 Typical exam and tutorial questions with minimal computation
• • A.2 Generic questions
• • A.3 Case study based questions for use with assignments

Chapter Appendix B: Further reading

• • B.1 Introduction
• • B.2 Guidance for the lecturer/reader
• • B.3 Simple variations on the basic algorithm
• • B.4 Parametric approaches to solving quadratic programming
• • B.5 Prediction mismatch and the link to feedforward design in MPC
• • B.6 Robust MPC: ensuring feasibility in the presence of uncertainty
• • B.7 Invariant sets and predictive control
• • B.8 Conclusions

Chapter Appendix C: Notation, models and useful background

• • C.1 Guidance for the lecturer/reader
• • C.2 Notation for linear models
• • C.3 Minimization of functions of many variables
• • C.4 Common notation