Robust and Adaptive Model Predictive Control of Nonlinear Systems [Bookshelf]

Abstract

This book provides a comprehensive study of nonlinear adaptive robust model predictive control (MPC). Chapters 2–5 present a framework for the analysis and synthesis of nonlinear robust MPC. This framework includes the treatment of robustness, computation methods, and performance improvement. Chapters 6–7 show how to develop the basic ideas for the design and analysis of the nonlinear adaptive robust MPC. One of the key techniques is the set-based approach, in which the internal model identifier allows the MPC to compensate for future changes in the parameter estimates and uncertainty associated with the unknown model parameters. Chapters 8–12 illustrate how to implement the synthesis approaches for nonlinear adaptive robust MPC, and a robust adaptive economic MPC is also proposed. This text also gives a finite-time identification method, which can be used to estimate the unknown parameters in finite time, provided a persistence of excitation (PE) condition is satisfied. This identification method is particularly effective in the online implementation of MPC. The early chapters study continuous-time systems, and Chapters 13–14 extend the set-based estimation and robust adaptive MPC to discrete-time problems. While adaptive robust MPC is an improvement on robust MPC, this book shows that feedback MPC can be used to improve the open-loop MPC. At each sampling instant, a sequence of parameter estimates can be performed/invoked to improve the control performance. Economic MPC is also incorporated so as to improve the control performance in a broader way. This book is intended for someone learning functions of a complex variable and who enjoys using Matlab. It will enhance the experience of learning complex-variable theory and will strengthen the knowledge of someone already trained in this branch of advanced calculus. Supplying students with a bridge between the functions of complex-variable theory and Matlab, this supplemental text enables instructors to easily add a Matlab component to their complex-variables courses. The book shows students how Matlab can be a powerful learning aid in such staples of complex-variable theory as conformal mapping, infinite series, contour integration, and Laplace and Fourier transforms. In addition to Matlab programming problems, the text includes many examples in each chapter along with Matlab code.