Model Predictive Control Theory Computation And Design 2nd Edition 2nd Edition Rawlings James B by Rawlings James B., Mayne David Q., Diehl Moritz M. 9780975937730, 0975937731 instant download after payment.

In the eight years since the publication of the first edition, the field
of model predictive control (MPC) has seen tremendous progress. First
and foremost, the algorithms and high-level software available for solving challenging nonlinear optimal control problems have advanced significantly. For this reason, we have added a new chapter, Chapter 8,
“Numerical Optimal Control,” and coauthor, Professor Moritz M. Diehl.
This chapter gives an introduction into methods for the numerical solution of the MPC optimization problem. Numerical optimal control
builds on two fields: simulation of differential equations, and numerical optimization. Simulation is often covered in undergraduate courses
and is therefore only briefly reviewed. Optimization is treated in much
more detail, covering topics such as derivative computations, Hessian
approximations, and handling inequalities. Most importantly, the chapter presents some of the many ways that the specific structure of optimal control problems arising in MPC can be exploited algorithmically.
We have also added a software release with the second edition of
the text. The software enables the solution of all of the examples and
exercises in the text requiring numerical calculation. The software is
based on the freely available CasADi language, and a high-level set of
Octave/MATLAB functions, MPCTools, to serve as an interface to CasADi.
These tools have been tested in several MPC short courses to audiences
composed of researchers and practitioners. The software can be downloaded from www.chemengr.ucsb.edu/~jbraw/mpc.
In Chapter 2, we have added sections covering the following topics:
• economic MPC
• MPC with discrete actuators
We also present a more recent form of suboptimal MPC that is provably robust as well as computationally tractable for online solution of
nonconvex MPC problems.
In Chapter 3, we have added a discussion of stochastic MPC, which
has received considerable recent research attention.
In Chapter 4, we have added a new treatment of sta