Adaptive bounding techniques for stable neural control systems
SourceProceedings of the IEEE Conference on Decision and Control
Proceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4)
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This paper considers the design of stable adaptive neural controllers for uncertain nonlinear dynamical systems with unknown nonlinearities. The Lyapunov synthesis approach is used to develop state-feedback adaptive control schemes based on a general class of nonlinearly parametrized neural network models. The key assumptions are that the system uncertainty satisfies a 'strict-feedback' condition and that the network reconstruction error and higher-order terms (with respect to the parameter estimates) satisfy certain bounding conditions. An adaptive bounding design is used to show that the overall neural control system guarantees semi-global uniform ultimate boundedness within a neighborhood of zero tracking error.
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