Advances in nonlinear predictive control

A survey on stability and optimality

Wook Hyun Kwon, SooHee Han, Choon Ki Ahn

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Some recent advances in stability and optimality for the nonlinear receding horizon control (NRHC) or the nonlinear model predictive control (NMPC) are assessed. The NRHCs with terminal conditions are surveyed in terms of a terminal state equality constraint, a terminal cost, and a terminal constraint set. Other NRHCs without terminal conditions are surveyed in terms of a control Lyapunov function (CLF) and cost monotonicity. Additional approaches such as output feedback, fuzzy, and neural network are introduced. This paper excludes the results for linear receding horizon controls and concentrates only on the analytical results of NRHCs, not including applications of NRHCs. Stability and optimality are focused on rather than robustness.

Original languageEnglish
Pages (from-to)15-22
Number of pages8
JournalInternational Journal of Control, Automation and Systems
Volume2
Issue number1
Publication statusPublished - 2004 Mar 1
Externally publishedYes

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Model predictive control
Lyapunov functions
Costs
Neural networks
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Keywords

  • Control lyapunov
  • Cost monotonicity
  • Dual-mode control
  • Nonlinear model predictive control (NMPC)
  • Nonlinear predictive control (NPC)
  • Nonlinear receding horizon control (NRHC)
  • Terminal cost
  • Terminal state equality

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Advances in nonlinear predictive control : A survey on stability and optimality. / Kwon, Wook Hyun; Han, SooHee; Ahn, Choon Ki.

In: International Journal of Control, Automation and Systems, Vol. 2, No. 1, 01.03.2004, p. 15-22.

Research output: Contribution to journalArticle

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