A new adaptive fuzzy controller with saturation employing influential rule search scheme (IRSS)
International Journal of Knowledge-based and Intelligent Engineering Systems
Proceedings of the International Conference on Advances in Computing, Communication and Control
A Transductive Neuro-Fuzzy Force Control: An Ethernet-Based Application to a Drilling Process
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Nonlinear internal model control based on transformed fuzzy hyperbolic model
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
International Journal of Applied Mathematics and Computer Science
A transductive neuro-fuzzy controller: application to a drilling process
IEEE Transactions on Neural Networks
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An approximate internal model-based neural control (AIMNC) strategy is proposed for unknown nonaffine nonlinear discrete processes under disturbed environment. The proposed control strategy has some clear advantages in respect to existing neural internal model control methods. It can be used for open-loop unstable nonlinear processes or a class of systems with unstable zero dynamics. Based on a novel input-output approximation, the proposed neural control law can be derived directly and implemented straightforward for an unknown process. Only one neural network needs to be trained and control algorithm can be directly obtained from model identification without further training. The stability and robustness of a closed-loop system can be derived analytically. Extensive simulations demonstrate the superior performance of the proposed AIMNC strategy