An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators
Neural Computing and Applications
Observer-based adaptive control of robot manipulators: Fuzzy systems approach
Applied Soft Computing
CMAC-based neuro-fuzzy approach for complex system modeling
Neurocomputing
H∞ reinforcement learning control of robot manipulators using fuzzy wavelet networks
Fuzzy Sets and Systems
Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems
Information Sciences: an International Journal
Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm
Expert Systems with Applications: An International Journal
Standalone CMAC control system with online learning ability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Radial basis function neural network-based adaptive critic control of induction motors
Applied Soft Computing
Adaptive dynamic RBF neural controller design for a class of nonlinear systems
Applied Soft Computing
Fuzzy model reference adaptive control
IEEE Transactions on Fuzzy Systems
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
Engineering Applications of Artificial Intelligence
Neural-network hybrid control for antilock braking systems
IEEE Transactions on Neural Networks
Constructive feedforward neural networks using Hermite polynomial activation functions
IEEE Transactions on Neural Networks
RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology
IEEE Transactions on Neural Networks
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This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional-integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.