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Nonlinear model predictive control with regulable computational cost
He YQ(何玉庆); Han JD(韩建达)
Indexed BySCI ; EI
EI Accession number20120514731492
WOS IDWOS:000299468700028
Contribution Rank1
KeywordControl Lyapunov Functions Nonlinear Model Predictive Control Input Constraints
AbstractNonlinear model predictive control (NMPC) suffers from problems of closed loop instability and huge computational burden, which greatly limit its applications in real plants. In this paper, a new NMPC algorithm, whose stability is robust with respect to regulable computational cost, is presented. First, a new generalized pointwise min-norm (GPMN) control, as well as its analytic form considering a super-ball type input constraint, is given. Second, the GPMN controller is integrated into a normal NMPC algorithm as a structure of control input profile to be optimized, called GPMN enhanced NMPC (GPMN-ENMPC). Finally, a numerical example is presented and simulation results exhibit the advantage of the GPMN-ENMPC algorithm: computational cost can be regulated according to the computational resources with guaranteed stability.
WOS HeadingsScience & Technology ; Technology
WOS SubjectAutomation & Control Systems
WOS Research AreaAutomation & Control Systems
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Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorHe YQ(何玉庆)
AffiliationState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, 110016, Shenyang, China
Recommended Citation
GB/T 7714
He YQ,Han JD. Nonlinear model predictive control with regulable computational cost[J]. ASIAN JOURNAL OF CONTROL,2012,14(1):300-307.
APA He YQ,&Han JD.(2012).Nonlinear model predictive control with regulable computational cost.ASIAN JOURNAL OF CONTROL,14(1),300-307.
MLA He YQ,et al."Nonlinear model predictive control with regulable computational cost".ASIAN JOURNAL OF CONTROL 14.1(2012):300-307.
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