Variable Neural Adaptive Robust Control: A Switched System Approach
Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1182900
- Report Number(s):
- PNNL-SA-90777
- Journal Information:
- IEEE Transactions on Neural Networks and Learning Systems, 26(5):903-915, Journal Name: IEEE Transactions on Neural Networks and Learning Systems, 26(5):903-915
- Country of Publication:
- United States
- Language:
- English
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