Stable adaptive systems
Composite adaptive control of robot manipulators
Automatica (Journal of IFAC)
Universal approximation using radial-basis-function networks
Neural Computation
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Robust adaptive control
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximation Techniques
Adaptive output feedback control of nonlinear systems using neural networks
Automatica (Journal of IFAC)
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
Dynamic structure neural networks for stable adaptive control of nonlinear systems
IEEE Transactions on Neural Networks
Output feedback control of nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
Adaptive observer backstepping control using neural networks
IEEE Transactions on Neural Networks
Adaptive neural control of uncertain MIMO nonlinear systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A Q-modification neuroadaptive control architecture for discrete-time systems
IEEE Transactions on Neural Networks
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This paper develops a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture involving additional terms in the update laws that are constructed using a moving time window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress and cancel system uncertainty without the need for persistency of excitation. A nonlinear parametrization of the system uncertainty is considered and state and output feedback neuroadaptive controllers are developed. To illustrate the efficacy of the proposed approach we apply our results to a spacecraft model with unknown moment of inertia and compare our results with standard neuroadaptive control methods.