Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Brains, Behavior and Robotics
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
Stability of Adaptive Controllers
Stability of Adaptive Controllers
Robust Backstepping Control of Robotic Systems Using Neural Networks
Journal of Intelligent and Robotic Systems
Performance of Nonlinear Approximate Adaptive Controllers
Performance of Nonlinear Approximate Adaptive Controllers
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximation Techniques
Feedback error learning and nonlinear adaptive control
Neural Networks
Optimal design of CMAC neural-network controller for robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Robust backstepping control of nonlinear systems using neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
A novel Takagi-Sugeno-based robust adaptive fuzzy sliding-mode controller
IEEE Transactions on Fuzzy Systems
Uniformly ultimately bounded fuzzy adaptive tracking controllers for uncertain systems
IEEE Transactions on Fuzzy Systems
Stable adaptive fuzzy control of nonlinear systems
IEEE Transactions on Fuzzy Systems
Paper: Instability analysis and improvement of robustness of adaptive control
Automatica (Journal of IFAC)
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Stable neural controller design for unknown nonlinear systems using backstepping
IEEE Transactions on Neural Networks
Direct adaptive NN control of a class of nonlinear systems
IEEE Transactions on Neural Networks
Robust and adaptive backstepping control for nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
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Aiming to eliminate the bursting phenomena in low-gain approximate-adaptive controls that utilize local basis functions, this work proposes a new robust adaptation method. The bursting phenomena occurs when the approximator's adaptive parameters (fuzzy centers or neural weights) drift to large values, eventually causing a sudden increase in state error. The existence of bursting often prevents universal approximators with local functions from controlling non-minimum phase systems, where bursting is associated with excitation of a natural frequency. The proposed solution adds two additional approximators to estimate each nonlinear function. One learns the output of the approximator used in the control signal. The other stores in memory the best weights found so far in the training. These parallel representations of the data guide the stable online training and prevent drift of the adaptive parameters. Simulation results with a generic nonlinear system illustrate the expected improvement in qualitative behavior over traditional robust methods leakage, e-modification, and deadzone when gains are restricted. An experiment with a planar two-link flexible-joint robot confirms the expected improvement in behavior, the new method prevents bursting without large sacrifice in performance.