Model Predictive Control (MPC) is unusual in receiving on-going interest in both industrial and academic circles. Issues such as plant optimization and constrained control which are critical to industrial engineers are naturally embedded in its designs.

Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages:

• continuous- and discrete-time MPC problems solved in similar design frameworks;

• a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and

• a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters.

After the theoretical presentation, detailed coverage is given to three industrial applications: a food extruder, a motor and a magnetic bearing system. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained.

The technical contents of this book, mainly based on advances in MPC using state-space models and basis functions – to which the author is a major contributor, will be of interest to control researchers and practitioners, especially of process control. From a pedagogical standpoint, this volume includes numerous simple analytical examples and every chapter contains problems and MATLAB® programs and exercises to assist the student.

模型预测控制（MPC）在工业界和学术界都受到了持续的关注，这很不寻常。对工业工程师来说至关重要的工厂优化和约束性控制等问题自然而然地嵌入到其设计中。

使用MATLAB®的模型预测控制系统设计与实现》提出了使用基函数设计和实现MPC系统的方法，这些方法具有以下优点。

- 在类似的设计框架中解决连续和离散时间的MPC问题。

- 控制轨迹的简明参数化表示产生了计算效率高的算法和更好的在线性能；以及

- 一个更普遍的MPC设计的离散时间表示，对于适当的参数选择，它与传统的方法相同。

在理论介绍之后，详细介绍了三个工业应用：食品挤出机、电机和磁性轴承系统。还介绍和解释了通常与MPC的核心优化算法有关的二次编程的主题。

本书的技术内容主要基于使用状态空间模型和基函数的MPC的进展--作者是其中的主要贡献者，将对控制研究人员和从业人员，特别是过程控制的人员感兴趣。从教学的角度来看，本卷包括许多简单的分析例子，每一章都包含问题和MATLAB®程序和练习，以帮助学生。