A neural network robust controller (NNRC) to enhance the robustness of the conventional feedback controller is proposed in this paper. The control system consists of an optimal conventional feedback controller and a neural network robust controller in parallel to the controlled system. The optimal controller is used to guarantee the stability of the whole control system and present the optimal ...

This paper proposes the neural network solution to the indirect vector control of three phase induction motor including an adaptive neuro fuzzy controller. The basic equations and elements of the indirect vector control scheme are given. The proposed control scheme is realized by an adaptive neuro-fuzzy controller and two feed forward neural network. The neuro-fuzzy controller incorporates fuzz...

This paper presents the application of a neural network controller to the problem of active drag reduction in a fully turbulent 3D fluid flow regime. The neural network learns a function nearly identical to an analytically derived control law. We then demonstrate the ability of a neural controller to maintain a drag-reduced flow in a fully turbulent fluid simulation. Finally we examine the amou...

This paper presents a control scheme of a neural network for a DC-DC Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characterist...

In this paper, a second order plant is considered for identification using NN-predictive control technique. The neural network based predictive controller is configured based on MATLAB 7.0. This neural network controller uses a neural network model of plant. It predicts the future performance of the actual plant. The controller uses to calculate the control input. The control input will optimiz...

This article presents numerical studies on semi-active seismic response control of structures equipped with Magneto-Rheological (MR) dampers. A multi-layer artificial neural network (ANN) was employed to mitigate the influence of time delay, This ANN was trained using data from the El-Centro earthquake. The inputs of ANN are the seismic responses of the structure in the current step, and the ou...

• “An Efficient Training Technique for a Neural-Network Controller for Seismically Excited Structures,” (reference [54]) by Liut et al. • “An overview of Some Non-Traditional Neural-Network Training Strategies for Seismic Response Suppression of Building Structures,” (reference [56]) by Liut et al. • “A Modified Gradient-Search Training Technique for Neural-Network Structural Control,” (referen...

 Abstract—several neural networks controllers for robotics manipulators have been developed during the last decades due to their capability to learn the dynamics properties and the improvements in the global stability of the system. In this paper, two control and identification schemes for a two links robotic manipulator implementing neural networks are presented. A multilayer feedforward neu...

A new adaptive multiple-controller is proposed incorporating a neural network based Generalized Learning Model (GLM). The GLM assumes that the unknown complex plant is represented by an equivalent stochastic model consisting of a linear time-varying sub-model plus a Radial Basis Function (RBF) neural-network based learning sub-model . The proposed non-linear multiple-controller methodology prov...

the control of fluidized-bed operations processes is still one of the major areas of research due to the complexity of the process and the inherent nonlinearity and varying dynamics involved in its operation. there are varieties of problems in chemical engineering that can be formulated as nonlinear programming (nlps). the quality of the developed solution significantly affects the performance ...