Advanced control methods for industrial process control

作     者：Airikka, P 

作者机构：Timberjack Finland 

出 版 物：《COMPUTING & CONTROL ENGINEERING JOURNAL》 (IEE Comput. Control Eng.)

年 卷 期：2004年第15卷第3期

页      面：18-23页

核心收录：

主　　题：adaptive control neural network based control industrial process control predictive control multivariable control systems neurocontrollers Control technology and theory Stability in control theory advanced control method Specific control systems process control robust control model predictive control Control engineering computing Control in industrial production systems fuzzy control Optimal control Industrial processes optimal control multivariable control 

摘      要：Advanced control methods involve more complex calculations than the conventional PID controller algorithm. Often, advanced control is a high-level control procedure that takes care of sub-processes controlling low- level unit control loops such as PID controllers. In this case, advanced control strategy aims to fulfill economic objectives by providing appropriate set points for the lower-level control loops to minimize a given performance criterion. The use of process models is a common characteristic for advanced control methods. Advanced control relies strongly on process models that describe the process behavior. The models try to capture the essence of the process information. We give an overview of the seven advanced control method: adaptive control, multivariable control, model predictive control, fuzzy control, robust control, neural network based control and optimal control.