Adaptive Predictive Control-A Data-Driven Closed-loop Subspace Identification Approach

摘要：

　　This paper presents a data-driven adaptive predictive control method using closed-loop subspace identification.As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace identification algorithm driven by input-output data.Taking advantage of transformational system model, the closed-loop data is effectively processed in this subspace algorithm.By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given.Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance.The problems of online excitation data inaccuracy and closed-loop identification in adaptive control are well solved in the proposed method Simulation results show the efficiency of this method .

关键词： Data-driven approach Model predictive control Closed-loop subspace identification Subspace matrices Adaptive mechanism Data inspection strategy

作者: Xiaosuo Luo

作者单位: School of Automation, Chongqing University, Chongqing, China

会议类型: 国内会议

会议名称: 西南大学2014年全国博士生学术论坛(电子技术与信息科学领域)

会议地点: 重庆

会议语种:英文

页码: 162-179

在线出版日期: 2014-12-01（万方平台首次上网日期，不代表论文的发表时间）