里面有关于国外模型预测控制大牛的模型预测控制PPT讲义，该大牛是MATLAB MPC工具箱创建人，他的许多论文也是关于模型预测控制。

这是模型预测控制的国外优秀教材，精简的阐述了利用matlab来实现模型预测控制，并有实例代码

MPC,模型预测控制，J.B. Rawlings 和D.Q.Mayne所写。这两位所站的高度和视野广度才能够写出这本优秀的教材来。我日常以这本教材作为辅助参考书使用。

模型预测、自动控制领域大牛Alberto Bemporad的博士课程讲义，内容为MPC模型预测控制，讲解了MPC的基本概念，线性系统的MPC理论

Model predictive control (MPC) has a long history in the field of control en- gineering. It is one of the few areas that has received on-going interest from researchers in both the industrial and academic communities. Four major as- pects of model predictive control make the design methodology attractive to both practitioners and academics. The first aspect is the design formulation, which uses a completely multivariable system framework where the perfor- mance parameters of the multivariable control system are related to the engi- neering aspects of the system; hence, they can be understood and ‘tuned’ by engineers. The second aspect is the ability of the method to handle both ‘soft’ constraints and hard constraints in a multivariable control framework. This is particularly attractive to industry where tight profit margins and limits on the process operation are inevitably present. The third aspect is the ability to perform on-line process optimization. The fourth aspect is the simplicity of the design framework in handling all these complex issues. This book gives an introduction to model predictive control, and recent developments in design and implementation. Beginning with an overview of the field, the book will systematically cover topics in receding horizon con- trol, MPC design formulations, constrained control, Laguerre-function-based predictive control, predictive control using exponential data weighting, refor- mulation of classical predictive control, tuning of predictive control, as well as simulation and implementation using MATLAB and SIMULINK as a platform. Both continuous-time and discrete-time model predictive control is presented in a similar framework.

Nonlinear Model Predictive Control_ Theory and Algorithms：second edition

A timely introduction to current research on PID and predictive control by one of the leading authors on the subject PID and Predictive Control of Electric Drives and Power Supplies using MATLAB/Simulink examines the classical control system strategies, such as PID control, feed-forward control and cascade control, which are widely used in current practice.02 The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking the reader from the fundamentals through to more sophisticated design and analysis.

模型预测控制书籍推荐（全是外文好书）有些含MATLAB例子

很难下载到的预测控制书籍资源，5分拿去把。Rawlings是预测控制界的大牛，懂的人自然都懂。

Model Predictive Control:Theory, Computation, and Design,2nd Edition. James B. Rawlings, David Q. Mayne, Moritz M. Diehl. Chapter 1 is introductory. It is intended for graduate students in engineering who have not yet had a systems course. But it serves a second purpose for those who have already taken the first graduate systems course. It derives all the results of the linear quadratic regulator and optimal Kalman filter using only those arguments that extend to the nonlinear and constrained cases to be covered in the later chapters. Instructors may find that this tailored treatment of the introductory systems material serves both as a review and a preview of arguments to come in the later chapters. Chapters 2-4 are foundational and should probably be covered in any graduate level MPC course. Chapter 2 covers regulation to the origin for nonlinear and constrained systems. This material presents in a unified fashion many of the major research advances in MPC that took place during the last 20 years. It also includes more recent topics such as regulation to an unreachable setpoint that are only now appearing in the research literature. Chapter 3 addresses MPC design for robustness, with a focus on MPC using tubes or bundles of trajectories in place of the single nominal trajectory. This chapter again unifies a large body of research literature concerned with robust MPC. Chapter 4 covers state estimation with an emphasis on moving horizon estimation, but also covers extended and unscented Kalman filtering, and particle filtering. Chapters 5-7 present more specialized topics. Chapter 5 addressesthe special requirements of MPC based on output measurement instead of state measurement. Chapter 6 discusses how to design distributed MPC controllers for large-scale systems that are decomposed into many smaller, interacting subsystems. Chapter 7 covers the explicit optimal control of constrained linear systems. The choice of coverage of these three chapters may vary depending on the instructor's or student's own research interests. Three appendices are included, again, so that the reader is not sent off to search a large research literature for the fundamental arguments used in the text. Appendix A covers the required mathematical background. Appendix B summarizes the results used for stability analysis including the various types of stability and Lyapunov function theory. Since MPC is an optimization-based controller, Appendix C covers the relevant results from optimization theory.