Simultaneous constrained model predictive control and identification of DARX processes

作     者：Shouche, M Genceli, H Vuthandam, P Nikolaou, M 

作者机构：Texas A&M Univ Dept Chem Engn College Stn TX 77843 USA Univ Houston Dept Chem Engn Houston TX 77204 USA 

出 版 物：《AUTOMATICA》 (自动学)

年 卷 期：1998年第34卷第12期

页      面：1521-1530页

核心收录：

学科分类：0711[理学-系统科学] 0808[工学-电气工程] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

基　　金：Acknowledgements—Partial Þnancial support from the 1993 Texas Advanced Technology Program/ Development is gratefully acknowledged 

主　　题：adaptive control predictive control closed-loop identification semidefinite programming 

摘      要：In this work, we formulate a new approach to simultaneous constrained model predictive control and identification (MPCI). The proposed approach relies on the development of a persistent excitation (PE) criterion for processes described by DARX models. That PE criterion is used as an additional constraint in the standard on-line optimization of MPC. The resulting on-line optimization problem of MPCI is handled by successively solving a series of semi-definite programming problems. Advantages of MPCI in comparison to other closed-loop identification methods are (a) Constraints on process inputs and outputs are handled explicitly, (b) Deterioration of output regulation is kept to a minimum, while closed-loop identification is performed. The applicability of the method is illustrated by a number of simulation studies. Theoretical and computational issues for further investigation are suggested. (C) 1998 Elsevier Science Ltd. All rights reserved.