In this chapter, three real-world control problems, namely anesthesia, magnetic rigid spacecraft and tractor-implement system are studied by using SMC theory-based learning algorithms for T2FNNs. For all the systems, the FEL scheme is preferred in which a conventional controller (PD, etc.) works in parallel with an intelligent structure (T1FNN, T2FNN, etc.). The proposed learning algorithms have been shown to be able to control these real-world example problems with satisfactory performance. Note that the proposed control algorithms do not need a priori knowledge of the system to be controlled.
Elliptic membership function
Tractor and implement
Get Fuzzy Neural Networks for Real Time Control Applications now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.