Assessment and Future Directions of Nonlinear Model Predictive Control

1. Front Matter

   Pages I-XII

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2. Foundations and History of NMPC

   1. Nonlinear Model Predictive Control: An Introductory Review

      • Eduardo F. Camacho, Carlos Bordons

      Pages 1-16

3. Theoretical Aspects of NMPC

   1. Hybrid MPC: Open-Minded but Not Easily Swayed

      • S. Emre Tuna, Ricardo G. Sanfelice, Michael J. Messina, Andrew R. Teel

      Pages 17-34

   2. Conditions for MPC Based Stabilization of Sampled-Data Nonlinear Systems Via Discrete-Time Approximations

      • Eva Gyurkovics, Ahmed M. Elaiw

      Pages 35-48

   3. A Computationally Efficient Scheduled Model Predictive Control Algorithm for Control of a Class of Constrained Nonlinear Systems

      • Mayuresh V. Kothare, Zhaoyang Wan

      Pages 49-62

   4. The Potential of Interpolation for Simplifying Predictive Control and Application to LPV Systems

      • John Anthony Rossiter, Bert Pluymers, Bart De Moor

      Pages 63-76

   5. Techniques for Uniting Lyapunov-Based and Model Predictive Control

      • Prashant Mhaskar, Nael H. El-Farra, Panagiotis D. Christofides

      Pages 77-91

   6. Discrete-Time Non-smooth Nonlinear MPC: Stability and Robustness

      • M. Lazar, W. P. M. H. Heemels, A. Bemporad, S. Weiland

      Pages 93-103

   7. Model Predictive Control for Nonlinear Sampled-Data Systems

      • L. Grüne, D. Nešić, J. Pannek

      Pages 105-113

   8. Sampled-Data Model Predictive Control for Nonlinear Time-Varying Systems: Stability and Robustness

      • Fernando A. C. C. Fontes, Lalo Magni, Eva Gyurkovics

      Pages 115-129

   9. On the Computation of Robust Control Invariant Sets for Piecewise Affine Systems

      • T. Alamo, M. Fiacchini, A. Cepeda, D. Limon, J. M. Bravo, E. F. Camacho

      Pages 131-139

   10. Nonlinear Predictive Control of Irregularly Sampled Data Systems Using Identified Observers

       • Meka Srinivasarao, Sachin C. Patwardhan, R. D. Gudi

       Pages 141-149

   11. Nonlinear Model Predictive Control: A Passivity-Based Approach

       • Tobias Raff, Christian Ebenbauer, Prank Allgöwer

       Pages 151-162

4. Numerical Aspects of NMPC

   1. Numerical Methods for Efficient and Fast Nonlinear Model Predictive Control

      • Hans Georg Bock, Moritz Diehl, Peter Kühl, Ekaterina Kostina, Johannes P. Schiöder, Leonard Wirsching

      Pages 163-179

   2. Computational Aspects of Approximate Explicit Nonlinear Model Predictive Control

      • Alexandra Grancharova, Tor A. Johansen, Petter Tøndel

      Pages 181-192

   3. Towards the Design of Parametric Model Predictive Controllers for Non-linear Constrained Systems

      • V. Sakizlis, K. I. Kouramas, N. P. Faisca, E. N. Pistikopoulos

      Pages 193-205

   4. Interior-Point Algorithms for Nonlinear Model Predictive Control

      • Adrian G. Wills, William P. Heath

      Pages 207-216

   5. Hard Constraints for Prioritized Objective Nonlinear MPC

      • Christopher E. Long, Edward P. Gatzke

      Pages 217-228

   6. A Nonlinear Model Predictive Control Framework as Free Software: Outlook and Progress Report

      • Andrey Romanenko, Lino O. Santos

      Pages 229-238

5. Robustness, Robust Design, and Uncertainty

   1. Robustness and Robust Design of MPC for Nonlinear Discrete-Time Systems

      • Lalo Magni, Riccardo Scattolini

      Pages 239-254

Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today’s computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.

• Model Predictive Control
• NMPC
• Obstacle Avoidance
• control
• feedback
• linear optimization
• path planning
• process control
• uncertainty

“It is a wide ranging text covering such areas as hybrid systems, constrained control, LPV systems, nonlinear discrete-time systems and many others. … This is a valuable contribution to the subject and is suitable for researchers and engineers involved in all aspects of nonlinear MPC design and implementation.” (ACTC applied control technology consortium, actc-control.com, March, 2016)

• Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany

  Rolf Findeisen, Frank Allgöwer

• Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, USA

  Lorenz T. Biegler

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