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Reformulated principle of fault estimation design for one class of first order continuous-time nonlinear system is treated in this paper, where a neural network is regarded as model-free fault approximator. The problem addressed is presented as approach based on sliding mode methodology with combination of radial basis function neural network to design robust nonlinear fault estimation. The method...
An intelligent integral backstepping sliding mode control (IIBSMC) system using a multi-input multi-output (MIMO) recurrent neural network (RNN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties in this study. First, the dynamic model of the magnetic levitation system is derived. Then, an integral backstepping sliding mode control...
A Robust-Intelligent controller based on sliding mode control theory and neural network is presented to reduce the bullwhip effect in supply chain. A state space model used to design and evaluate the performance of the proposed controller. The neural network control strategy is studied to overcome the “chattering” of the sliding mode controller. The numerical simulations are curried out to check the...
A nonlinear speed controller for permanent magnet synchronous motor (PMSM) based on backstepping control, sliding mode approach and RBF network was designed. On the one hand, the proposed strategy adopts backstepping to guarantee the control precision. On the other hand, the combined RBF sliding mode method could enhance the robustness compare with conventional sliding mode control. A simulation model...
The problem of output tracking control for a class of multi-input multi-output uncertain systems is considered. A novel adaptive robust controller is proposed, which incorporates a variable-structure radial basis function (RBF) network to approximate unknown system dynamics. The RBF network can determine its structure on-line dynamically, where radial basis functions are added or removed to ensure...
In this paper we propose a mathematical model to describe the dynamics of neurotransmitter's active, available and reprocessed states. A set of nonlinear differential equations is obtained from neurophysiological and biochemical reasoning. To solve a tracking control problem, we study cellular feedback mechanisms that regulate the amount of neurotransmitters at distinct states. The neurotransmitters...
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