Neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
H∞ Control Design Using Dynamic Neural Networks
Neural Processing Letters
Global robust stabilizing control for a dynamic neural network system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Trajectory tracking based on differential neural networks for a class of nonlinear systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
IEEE Transactions on Neural Networks
Exponential stability analysis and impulsive tracking control of uncertain time-delayed systems
Journal of Global Optimization
Brief Robot discrete adaptive control based on dynamic inversion using dynamical neural networks
Automatica (Journal of IFAC)
Stable adaptive neuro-control design via Lyapunov function derivative estimation
Automatica (Journal of IFAC)
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The purpose of this paper is to design and rigorously analyze a tracking controller, based on a dynamic neural network model for unknown but affine in the control, multi input nonlinear dynamical systems, Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. The controller derived is smooth. No a priori knowledge of an upper bound on the “optimal” weights and modeling errors is required. Simulation studies are used, to illustrate and clarify the theoretical results