Model selection in neural networks
Neural Networks
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Direct adaptive fuzzy sliding mode control of arc furnace electrode regulator system
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive control using neural networks and approximate models
IEEE Transactions on Neural Networks
A new method for the control of discrete nonlinear dynamic systems using neural networks
IEEE Transactions on Neural Networks
Robust adaptive control of nonaffine nonlinear plants with small input signal changes
IEEE Transactions on Neural Networks
Output feedback control of a class of discrete MIMO nonlinear systems with triangular form inputs
IEEE Transactions on Neural Networks
An approximate internal model-based neural control for unknown nonlinear discrete processes
IEEE Transactions on Neural Networks
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The electrode regulator system is a complex system with multivariable, strong coupling and strong nonlinearity, and conventional control methods such as PID cannot meet the requirements. This paper proposes an adaptive neural network controller (ANNC) for electrode regulator system. An equivalent model in affine-like form is first derived as feedback linearization methods cannot be implemented for such systems. Then, adaptive control is implemented based on the affine-like equivalent model. Pretraining is not required and the weights of the neural networks (NNs) are directly updated online based on the input-output measurement. The robustness of the stability is established by the Lyapunov method. The proposed nonlinear controller is verified by computer simulations and experiments.