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Abstract
This paper describes the combination design of predictive functional control (PFC) and optimal linear quadratic (LQ) method for a kind of nonlinear process with output feedback coupling. In many existing control methods for this kind of nonlinear systems, the nonlinear part is either ignored or represented as a rough linear one when corresponding predictive control methods are designed. However, by assuming that the nonlinearity can be ignored or simplified to a linear time-varying part may not lead to the good control performance of subsequent linear control designs. The paper is a further investigation on this kind of systems, in which a procedure of PFC plus a modified optimal LQ control is developed. With respect to the proposed control strategy and the corresponding processes, the closed-loop performance is improved concerning tracking ability and disturbance rejection compared with previous predictive control methods. In addition, the proposed control is easy to implement as it selects a simple structure and a modification of the classical control scheme.
Acknowledgements
A part of this project was supported by the National Natural Science Foundation of China [grant number 61134007], [grant number 61273101], [grant number 61333009]; Hong Kong, Macao and Taiwan Science and Technology Cooperation Programme of China grant number 2013DFH10120]; National 973 Programme [grant number 2012CB821204].
Additional information
Notes on contributors
Ridong Zhang
Ridong Zhang received the PhD degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2007. He is currently an associate professor with the Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China. From 2012 to 2014, he was a postdoctoral researcher at the Chemical and Biomolecular Engineering Department, The Hong Kong University of Science and Technology, Hong Kong. His research interests include process modelling, model predictive control and nonlinear systems.
Renquan Lu
Renquan Lu received the PhD degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2004. He is currently a full professor with the Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China. He has published more than 30 journal papers in the fields of robust control and complex systems. His research interests include robust control, singular systems and complex systems.
Shuqing Wang
Shuqing Wang is a professor at Control Science and Engineering Department, Zhejiang University, Hangzhou, China. He has published more than 300 journal papers in the fields of process modelling, control and monitoring. His research interests include process control, computer control, data mining and nonlinear control.