A new pseudo-Gaussian-based recurrent fuzzy CMAC model for dynamic systems processing
International Journal of Systems Science
CMAC-based neuro-fuzzy approach for complex system modeling
Neurocomputing
Sliding-mode-based fuzzy CMAC Controller design for a class of uncertain nonlinear system
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Behavioral-fusion control based on reinforcement learning
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Standalone CMAC control system with online learning ability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Grey adaptive growing CMAC network
Applied Soft Computing
Q-Learning with FCMAC in multi-agent cooperation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Multi-agent congestion control for high-speed networks using reinforcement co-learning
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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A neuro fuzzy system which is embedded in the conventional control theory is proposed to tackle physical learning control problems. The control scheme is composed of two elements. The first element, the fuzzy sliding mode controller (FSMC), is used to drive the state variables to a specific switching hyperplane or a desired trajectory. The second one is developed based on the concept of the self organizing fuzzy cerebellar model articulation controller (FCMAC) and adaptive heuristic critic (AHC). Both compose a forward compensator to reduce the chattering effect or cancel the influence of system uncertainties. A geometrical explanation on how the FCMAC algorithm works is provided and some refined procedures of the AHC are presented as well. Simulations on smooth motion of a three-link robot is given to illustrate the performance and applicability of the proposed control scheme