Receding Horizon Control Model Predictive Control for State Models [Paperback]

Easy-to-follow learning structure makes absorption of advanced material as pain-free as possible

Introduces complete theories for stability and cost monotonicity for constrained and non-linear systems as well as for linear systems

In co-ordination with MATLAB^? files available from springeronline.com, exercises and examples give the student more practice in the predictive control and filtering techniques presented

Receding Horizon Control introduces the essentials of a successful feedback strategy that has emerged in many industrial fields: the process industries in particular. Receding horizon control (RHC) has a number of advantages over other types of control: easier computation than steady-state optimal control; greater adaptability to parametric changes than infinite horizon control; better tracking than PID and good constraint handling among others.

The text builds understanding starting with optimal controls for simple linear systems and working through constrained systems to nonlinear cases. RHC is applied to discrete-time systems for better understanding and easier computer application. Its diverse techniques are unified using the state-space framework. Worked examples and exercises throughout the book allow you to practise as you go and MATLAB^? files for the solution of selected examples can be downloaded from springeronline.com.

Graduate students following masters and doctoral courses in control theory and engineering will find Receding Horizon Control to be an excellent companion to tuition and research. Tutors and academics researching model predictive control can use this not only as a scholarly textbook but as a co-ordinated reference for its wide range of receding horizon schemes.