Description
Book SynopsisModern Predictive Control explains how MPC differs from other control methods in its implementation of a control action. Most importantly, MPC provides the flexibility to act while optimizingwhich is essential to the solution of many engineering problems in complex plants, where exact modeling is impossible.
The superiority of MPC is in its numerical solution. Usually, MPC is employed to solve a finite-horizon optimal control problem at each sampling instant and obtain control actions for both the present time and a future period. However, only the current control move is applied to the plant.
This complete, step-by-step exploration of various approaches to MPC:
- Introduces basic concepts of systems, modeling, and predictive control, detailing development from classical MPC to synthesis approaches
- Explores use of Model Algorithmic Control (MAC), Dynamic Matrix Control (DMC), Generalized Predic
Table of Contents
Systems, modeling and model predictive control. Model algorithmic control (MAC). Dynamic matrix control (DMC). Generalized predictive control (GPC). Two-step model predictive control. Sketch of synthesis approaches of MPC. State feedback synthesis approaches. Synthesis approaches with finite switching horizon. Open-loop optimization and closed-loop optimization in synthesis approaches. Output feedback synthesis approaches. Bibliography. Index.
