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This paper deals with the predictive control of discrete time nonlinear systems based on artificial neural networks. The system behavior is described by a neural networks model and the control law is obtained by minimizing a quadratic cost function. An adaptive variable control rate which is based on Lyapunov function candidate and assumes the closed loop stability is developed. A simulation example...
This paper proposes a new nonlinear unknown input observer. The observer design approach utilizes the first order Taylor expansion. The observer gains are then obtained by a systematic method. In this paper, we added some improvements to this method. The developed approach also can enable observer design for a large class of differentiable nonlinear systems. The necessary and sufficient conditions...
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