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- Title
Nonlinear model predictive control with regulable computational cost.
- Authors
He, Y. Q.; Han, J. D.
- Abstract
Nonlinear 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. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
- Subjects
PREDICTIVE control systems; AUTOMATIC control systems; ALGORITHMS; CONTROL theory (Engineering); CLOSED loop systems
- Publication
Asian Journal of Control, 2012, Vol 14, Issue 1, p300
- ISSN
1561-8625
- Publication type
Article
- DOI
10.1002/asjc.271