NDLI: Nonlinear Optimal Control : A Control Lyapunov Function and Receding Horizon

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Nonlinear Optimal Control : A Control Lyapunov Function and Receding Horizon

Content Provider	Semantic Scholar
Author	Primbs, Perspective James A. Nevisti, Vesna Two, John C. Doylez Abstract
Copyright Year	1999
Abstract	Two well known approaches to nonlinear control involve the use of control Lyapunov functions (CLFs) and receding horizon control (RHC), also known as model predictive control (MPC). The on-line Euler-Lagrange computation of receding horizon control is naturally viewed in terms of optimal control, whereas researchers in CLF methods have emphasized such notions as inverse optimality. We focus on a CLF variation of Sontag’s formula, which also results from a special choice of parameters in the so-called pointwise minnorm formulation. Viewed this way, CLF methods have direct connections with the Hamilton-Jacobi-Bellman formulation of optimal control. A single example is used to illustrate the various limitations of each approach. Finally, we contrast the CLF and receding horizon points of view, arguing that their strengths are complementary and suggestive of new ideas and opportunities for control design. The presentation is tutorial, emphasizing concepts and connections over details and technicalities.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.ajc.org.tw/pages/PAPER/1.1PD/2-14-24.PDF
Alternate Webpage(s)	http://www.cco.caltech.edu/~primbs/ajc_99.ps
Language	English
Access Restriction	Open
Subject Keyword	Bellman equation Common Look and Feel Computation Computation (action) Control engineering Euler Euler–Lagrange equation Gain Hamilton–Jacobi–Bellman equation Jacobi method Lyapunov fractal Maxima and minima Nonlinear system Online and offline Optimal control Plant Roots Point of View (computer hardware company) Recueil des historiens des croisades
Content Type	Text
Resource Type	Article

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