Theory -- The Essentials of Model Predictive Control (MPC) -- Dynamic Programming and MPC -- Set-valued and Lyapunov Methods for MPC -- Stochastic Model Predictive Control -- Moving Horizon Estimation -- Probing and Duality in Stochastic Model Predictive Control -- Economic Model Predictive Control: Some Design Tools and Analysis Techniques -- Nonlinear Predictive Control for Trajectory Tracking and Path Following: An Introduction and Perspective -- Hybrid Model Predictive Control -- Model Predictive Control of Polynomial Systems -- Distributed MPC for Large-Scale Systems -- Scalable MPC Design -- Computations -- Efficient Convex Optimization for Linear MPC -- Implicit Non-Convex Model Predictive Control -- Convexification and Real-Time Optimization for MPC with Aerospace Applications -- Explicit (Offline) Optimization for MPC -- Real-Time Implementation of Explicit Model Predictive Control -- Robust Optimization for MPC -- Scenario Optimization for MPC -- Nonlinear Programming Formulations for Nonlinear and Economic Model Predictive Control -- Applications -- Automotive Applications of Model Predictive Control -- Applications of MPC in the Area of Health Care -- Model Predictive Control for Power Electronics Applications -- Learning-based Fast Nonlinear Model Predictive Control for Custom-made 3D Printed Ground and Aerial Robots -- Applications of MPC to Building HVAC Systems -- Toward Multi-Layered MPC for Complex Electric Energy Systems -- Applications of MPC to Finance.

Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today. The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance. The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.