IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An observer-based neural networks control scheme for nonlinear systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
CMAC-based compensator for limiting bound required in supervisory control systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Multiple incremental fuzzy neuro-adaptive control of robot manipulators
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IEEE Transactions on Neural Networks
Multiple fuzzy neural networks modeling with sparse data
Neurocomputing
Neural networks controller for time-varying systems
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints
Automatica (Journal of IFAC)
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A new adaptive multiple neural network controller (AMNNC) with a supervisory controller for a class of uncertain nonlinear dynamic systems was developed in this paper. The AMNNC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple neural networks. The weighted sum of the multiple neural networks was used to approximate system nonlinearity for the given task. Each neural network represents the system dynamics for each task. For a job where some tasks are repeated but information on the load is not defined and unknown or varying, the proposed controller is effective because of its capability to memorize control skill for each task with each neural network. For a new task, most similar existing control skills may be used as a starting point of adaptation. With the help of a supervisory controller, the resulting closed-loop system is globally stable in the sense that all signals involved are uniformly bounded. Simulation results on a cartpole system for the changing mass of the pole were illustrated to show the effectiveness of the proposed control scheme for the comparison with the conventional adaptive neural network controller (ANNC).