Adaptive control of a nonlinear dc motor drive using recurrent neural networks
Applied Soft Computing
Direct neural network-based self-tuning control for a class of nonlinear systems
International Journal of Systems Science
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Interval Self-Organizing Map for Nonlinear System Identification and Control
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Laser beam pointing and stabilization by intensity feedback control
ACC'09 Proceedings of the 2009 conference on American Control Conference
A robust extended Elman backpropagation algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
On-line learning algorithm based on signal flow graph theory for PID neural networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
On the weight convergence of Elman networks
IEEE Transactions on Neural Networks
Adaptive robust NN control of nonlinear systems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Adaptive inverse control system based on least squares support vector machines
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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In this paper, we see adaptive control as a three-part adaptive-filtering problem. First, the dynamical system we wish to control is modeled using adaptive system-identification techniques. Second, the dynamic response of the system is controlled using an adaptive feedforward controller. No direct feedback is used, except that the system output is monitored and used by an adaptive algorithm to adjust the parameters of the controller. Third, disturbance canceling is performed using an additional adaptive filter. The canceler does not affect system dynamics, but feeds back plant disturbance in a way that minimizes output disturbance power. The techniques work to control minimum-phase or nonminimum-phase, linear or nonlinear, single-input-single-output (SISO) or multiple-input-multiple-ouput (MIMO), stable or stabilized systems. Constraints may additionally be placed on control effort for a practical implementation. Simulation examples are presented to demonstrate that the proposed methods work very well.