Bicycling Science [Book Review]
application of the computational algorithms. Such author insight would have been extremely valuable. From the standpoint of extending the applicability of the receding horizon optimal control to faster processes, computational complexity represents an especially critical area. In the text, input constraints are generally emphasized more than state constraints. However, other techniques, such as antiwindup compensation and gain scheduling, exist for dealing with pure input constraints. A discussion of the relative benefits of the techniques described in the text over those prevalent in the literature would have been beneficial. Overall, a deeper look at links or extensions to nonlinear systems with general state and input constraints as well as at the associated computational issues would have been valuable. To be fair, the book does provide pointers to the literature should the reader desire a thorough look at these and other topics. In summary, the book provides a self-contained, well-thought out, and accessible introduction to a wide range of topics and fundamental principles underlying receding horizon constrained control and estimation. The developments are presented at a sufficient level of detail and mathematical rigor to accomplish this objective. The book can be used as a textbook for a graduate-level course, for independent reading, or as a reference. Several other books have recently appeared on the subject of receding horizon optimal control for constrained systems, including [2] and [3]. While the present book has some overlap with these two texts in terms of its coverage (namely the consideration of discrete linear systems having input constraints), Constrained Control and Estimation stands on its own as a complete work and a rigorous yet accessible introductory text with unique perspectives on the application of convex optimization tools to systems having input constraints.