P
Petar V. Kokotovic
Researcher at University of California, Santa Barbara
Publications - 354
Citations - 41962
Petar V. Kokotovic is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Nonlinear system & Adaptive control. The author has an hindex of 83, co-authored 354 publications receiving 40395 citations. Previous affiliations of Petar V. Kokotovic include Washington State University & University of Illinois at Urbana–Champaign.
Papers
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Proceedings ArticleDOI
A note on a Lyapunov argument for stochastic gradient methods in the presence of noise
Ali H. Sayed,Petar V. Kokotovic +1 more
TL;DR: In this article, the authors employ energy-based arguments to establish a robustness and a convergence result for a gradient-descent adaptive law in the presence of both noisy measurements and uncertainty in the initial guess.
Journal ArticleDOI
Sensitivity Function Methods in Control System Education
TL;DR: In this paper, a testbed arc welding installation is used to demonstrate the practicality of the sensitivity methods in control education, beginning with the first course, and the inclusion of sensitivity methods is advocated.
Book ChapterDOI
Backstepping design of robust controllers for a class of nonlinear systems
TL;DR: A backstepping procedure for designing non-dynamic feedback compensators for a class of uncertain nonlinear systems whose uncertainties vanish at an equilibrium point and one for systems with more general types of uncertainties.
Some Problems in the Development of Adaptive Systems Using the Sensitivity Operator
TL;DR: Adaptive systems have been defined in a variety of ways but it is generally agreed that some degree of ignorance in the characteristics of the plant and/or the input signals must be involved in order to justify the adaptive approach.
Journal ArticleDOI
Stability bounds for slow adaptation: an integral manifold approach
B. Riedle,Petar V. Kokotovic +1 more
TL;DR: In this paper, a scheme for reduced order adaptive control in discrete time using slow adaptation and regressor filtering is presented. But the stability of slow adaptation does not rely on driving the tracking error to exactly zero and makes no assumption about the order of the plant, controller, or reference model.