本课程介绍了工业过程高级控制基本原理、基本结构、算法设计和适用范围等，重点讲解高级过程控制的基本原理和算法设计。首先，回顾了工业过程控制的发展过程，说明高级控制在过程控制中所处的地位及基本内容；其次，讲解工业过程的数学模型和数字滤波器的设计；最后，着重讲解几个比较成熟且在工业过程控制中比较行之有效的高级过程控制算法的基本原理、设计和工业应用方法，如PID控制及其参数自整定、多变量控制系统的关联分析与解耦控制、差拍控制系统、大纯滞后控制、软测量技术与推断控制、双重控制与非线性控制系统、自适应控制系统和预测控制。同时结合实时控制系统软件设计原理，介绍了几个高级过程控制的算法设计实例。《工业过程高级控制》的主要任务是在了解工业过程高级控制的原理及应用基础上，理解高级过程控制的基本原理、设计和应用，并掌握工业过程控制中几个成熟且行之有效的高级控制算法的设计和应用。

In the processing industry, controllers play a crucial role in keeping our plants running virtually everything from simply filling up a storage tank to complex separation processes and chemical reactors. The design procedures depend heavily on the dynamic model of the process to be controlled. In more advanced model-based control systems, the action taken by the controller actually depends on the model. Process models underpin most modern control approaches. Depending on the model forms, different controllers can be synthesised. Even the prevalent Proportional+Integral+Derivative (PID) algorithm can be designed from a model based perspective. The performance capabilities of PID algorithms are limited though. More sophisticated strategies, such as adaptive algorithms and predictive controllers have been proposed for improved process control. Due to the emphasis on Quality, Statistical Process Control (SPC) techniques are also experiencing a revival. In particular, attempts are being made to integrate traditional SPC practice with engineering feedback control techniques. Each of these strategies possesses respective merits. Of special significance is the recent attention paid to developing practicable nonlinear controllers, in recognition of the fact that many real processes are nonlinear and that adaptive systems may not be able to cope with significant nonlinearities. Cheap powerful computers and advances in the field of Artificial Intelligence are also making their impact. These reside at a higher level in the information management and process control hierarchy. Performing tasks that relate directly to overall plant management objectives, they effectively link plant business objectives with local unit operations. The result is an environment that is conducive to more consistent production. Modern process plants, designed for flexible production and to maximise recovery of energy and material, are becoming more complex. Process units are tightly coupled and the failure of one unit can seriously degrade overall productivity. This situation presents significant control problems. The literature on relevant control, monitoring, supervision and optimisation techniques is voluminous, each article exhorting a certain solution to a particular problem. However, it is generally acknowledged that there is currently not one technique that will solve all the control problems that can manifest in modern plants. Indeed, different plants have different requirements. A systematic studied approach to choosing pertinent techniques and their integration into a co-operative management and control system will significantly enhance plant operation and profitability. This is the goal of advanced process control. The content of the Advanced Control in Indutrial Process is mainly including the modeling of indutrial process, PID controller, multivariables interaction control system, Dalin algorithm, Smith predictve control, inner model control, soft monitor, inferential control, adaptive control and model predictive control etc. The mission of the Advanced Control in Indutrial Process is the development of methods and systems and the transfer of technology to enable process industry to operate their plants in the best possible way using advances in process engineering, control and systems science, and information and computer technology.