Neural Net-Based H∞ Control for a Class of Nonlinear Systems
Neural Processing Letters
International Journal of Systems Science
Neural Network-Based H ∞Filtering for Nonlinear Jump Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Modeling and control for nonlinear structural systems via a NN-based approach
Expert Systems with Applications: An International Journal
Multiobjective algebraic synthesis of neural control systems by implicit model following
IEEE Transactions on Neural Networks
Robust fuzzy controller design for nonlinear multiple time-delay systems by dithers
Fuzzy Sets and Systems
Stability analysis and robustness design of nonlinear systems: An NN-based approach
Applied Soft Computing
Reliable robust controller design for nonlinear state-delayed systems based on neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Stochastic optimal control of nonlinear jump systems using neural networks
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Robust H∞ fuzzy control of dithered chaotic systems
Neurocomputing
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We address a neural network-based control design for a discrete-time nonlinear system. Our design approach is to approximate the nonlinear system with a multilayer perceptron of which the activation functions are of the sigmoid type symmetric to the origin. A linear difference inclusion representation is then established for this class of approximating neural networks and is used to design a state feedback control law for the nonlinear system based on the certainty equivalence principle. The control design equations are shown to be a set of linear matrix inequalities where a convex optimization algorithm can be applied to determine the control signal. Further, the stability of the closed-loop is guaranteed in the sense that there exists a unique global attraction region in the neighborhood of the origin to which every trajectory of the closed-loop system converges. Finally, a simple example is presented so as to illustrate our control design procedure