Stable auto-tuning of hybrid adaptive fuzzy/neural controllers for nonlinear systems

Authors:
Hazem N. Nounou;Kevin M. Passino
Affiliations:
Department of Electrical Engineering, United Arab Emirates University, P.O. Box 17555, Al-Ain, UAE;Department of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210-1272, USA
Venue:
Engineering Applications of Artificial Intelligence
Year:
2005

Citing 4
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Abstract

In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, ''approximators'' such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as either the parameterized nonlinear controller or identifier model. In this paper, we present an algorithm to tune the adaptation gain for a gradient-based hybrid update law used for a class of nonlinear continuous-time systems in both direct and indirect cases. In our proposed algorithm, the adaptation gain is obtained by minimizing the instantaneous control energy. Finally, we will demonstrate the performance of the algorithm via a wing rock regulation example.