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Your search keyword '"MacKunis, William"' showing total 25 results
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25 results on '"MacKunis, William"'

24. Modular Adaptive Control of Uncertain Euler–Lagrange Systems With Additive Disturbances.

Author

Patre, Parag M., MacKunis, William, Dupree, Keith, and Dixon, Warren E.

Subjects
*ADAPTIVE control systems, *ARTIFICIAL neural networks, *ROBOTS, *ASYMPTOTIC expansions, *DAMPING (Mechanics), *LAGRANGE equations, *MODULAR design, *STOCHASTIC convergence
Abstract

A novel adaptive nonlinear control design is developed which achieves modularity between the controller and the adaptive update law. Modularity between the controller/update law design provides flexibility in the selection of different update laws that could potentially be easier to implement or used to obtain faster parameter convergence and/or better tracking performance. For a general class of linear-in-the-parameters (LP) uncertain Euler-Lagrange systems subject to additive bounded non-LP disturbances, the developed controller uses a model-based feedforward adaptive term in conjunction with the recently developed robust integral of the sign of the error (RISE) feedback term. Modularity in the adaptive feedforward term is made possible by considering a generic form of the adaptive update law and its corresponding parameter estimate. This generic form of the update law is used to develop a new closed-loop error system and stability analysis that does not depend on nonlinear damping to yield the modular adaptive control result. [ABSTRACT FROM AUTHOR]

Published
2011
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25. Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure.

Author

Patre, Parag M., MacKunis, William, Kaiser, Kent, and Dixon, Warren E.

Subjects
*ARTIFICIAL neural networks, *FEEDBACK control systems, *ADAPTIVE control systems, *UNCERTAINTY (Information theory), *ASYMPTOTIC theory of system theory, *FEEDFORWARD control systems, *NONLINEAR systems, *ALGORITHMS, *ROBUST control
Abstract

Abstract-The use of a neural network (NN) as a feedforward control element to compensate for nonlinear system uncertainties has been investigated for over a decade. Typical NN-based controllers yield uniformly ultimately bounded (UUB) stability results due to residual functional reconstruction inaccuracies and an inability to compensate for some system disturbances. Several researchers have proposed discontinuous feedback controllers (e.g., variable structure or sliding mode controllers) to reject the residual errors and yield asymptotic results. The research in this paper describes how a recently developed continuous robust integral of the sign of the error (RISE) feedback term can be incorporated with a NN-based feedforward term to achieve semi-global asymptotic tracking. To achieve this result, the typical stability analysis for the RISE method is modified to enable the incorporation of the NN-based feedforward terms, and a projection algorithm is developed to guarantee bounded NN weight estimates. [ABSTRACT FROM AUTHOR]

Published
2008
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