DocumentCode
724073
Title
An integrated state space partition and optimal control method of multi-model for nonlinear systems with state estimation
Author
Bingwei Xia ; Chunyue Song ; Bing Wu
Author_Institution
State Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China
fYear
2015
fDate
23-25 May 2015
Firstpage
1604
Lastpage
1609
Abstract
Linear analysis and control system are more popular and simple, which leads to the popularity of dealing with complex systems by multiple model approaches. To answer the question that how to obtain the optimal boundaries between the ranges where the submodels are active, a systematic method of state space partition based on closed-loop systems by hybrid systems optimal control theory is utilized. Additionally, a MLD-based MPC controller with corresponding optimal control problem is designed. In order to address the puzzle brought by unavailable states, this paper also develops an adaptive EKF estimator for the PWA model with fictitious noises. By applying this whole strategy to a CSTR process, the simulation demonstrates a satisfactory result.
Keywords
closed loop systems; linear systems; nonlinear control systems; optimal control; predictive control; state estimation; state-space methods; CSTR process; MLD-based MPC controller; PWA model; adaptive EKF estimator; closed-loop systems; complex systems; control system; fictitious noises; hybrid systems optimal control theory; integrated state space partition; linear analysis; multiple model approaches; nonlinear systems; optimal boundaries; optimal control method; state estimation; Adaptation models; Aerospace electronics; Mathematical model; Noise; Nonlinear systems; Optimal control; Adaptive EKF; Hybrid system optimal control; MLD-MPC controller; Multiple model approaches; State space partition;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location
Qingdao
Print_ISBN
978-1-4799-7016-2
Type
conf
DOI
10.1109/CCDC.2015.7162175
Filename
7162175
Link To Document