Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Stable adaptive systems
Multilayer feedforward networks are universal approximators
Neural Networks
Nonlinear control design: geometric, adaptive and robust
Nonlinear control design: geometric, adaptive and robust
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Gradient methods for the optimization of dynamical systems containing neural networks
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
Application of the recurrent multilayer perceptron in modeling complex process dynamics
IEEE Transactions on Neural Networks
Memory neuron networks for identification and control of dynamical systems
IEEE Transactions on Neural Networks
Stable indirect adaptive control based on discrete-time T--S fuzzy model
Fuzzy Sets and Systems
Adaptive Prediction of Stock Exchange Indices by State Space Wavelet Networks
International Journal of Applied Mathematics and Computer Science
Engineering Applications of Artificial Intelligence
| Hi-index | 22.14 |
An adaptive control technique for nonlinear stable plants with unmeasurable state is presented. It is based on a recurrent neural network employed as a dynamical model of the plant. Using this dynamical model, a feedback linearizing control is computed and applied to the plant. Parameters of the model are updated on-line to allow for partially unknown and time-varying plant. The stability of the scheme is shown theoretically, and its performance and limitations of the assumptions are illustrated in simulations. It is argued that appropriately structured recurrent neural networks can provide conveniently parameterized dynamic models for many nonlinear systems for use in adaptive control.