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Artículo: AMZ-0975937782
Model Predictive Control: Theory, Computation, and Design
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- 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 state estimation with persistent, bounded process and measurement disturbances. We have also removed the discussion of particle filtering. There are two reasons for this removal; first, we wanted to maintain a manageable total length of the text; second, all of the available sampling strategies in particle filtering come up against the acurse of dimensionality, which renders the state estimates inaccurate for dimension higher than about five. The material on particle filtering remains available on the text website. In Chapter 6, we have added a new section for distributed MPC of nonlinear systems. In Chapter 7, we have added the software to compute the critical regions in explicit MPC. Throughout the text, we support the stronger KL-definition of asymptotic stability, in place of the classical definition used in the first edition.
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