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Tracking Control of Robot Manipulators via Orthogo
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In this paper, an orthogonal functions neural network is used to achieve the control of nonlinear systems. The adaptive controller is constructed by using Chebyshev orthogonal polynomials neural network, which has advantages such as simple structure and fast convergence speed. The adaptive learning law of orthogonal neural network is derived to guarantee that the adaptive weight errors and tracking errors are bound by using Lyapunov stability theory. Simulation results are given for a two-link robot in the end of the paper, and the control scheme is validated.
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A New Buck-Boost DC/DC Converter of High Efficienc
4.0
In this paper, we study on a new buck-boost dc/dc converter of high efficiency by soft switching technique. The switching devices in the proposed converter are operated by soft switching with a new partial resonant circuit. The partial resonant circuit is designed to replacement of an energy storage inductor and a snubber circuit used in a conventional buck-boost converter, and then the configuration of the proposed converter is simplified. The switching control technique of the converter is also simplified for the switches to drive in a constant switching frequency. The results are that the switching power loss is very low and system efficiency is high. In addition, the output voltage of the converter is regulated by PWM control technique, and the discontinuous current flowing into the resonant inductor of the converter makes to simplify control method and control components. In comparison with the conventional buck
allpro 2 0
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A Particle Swarm Optimized Fuzzy Neural Network Fo
4.0
Since the excellent performances of treating non- obtained from PSO-FNN are much higher than the ones
linear data with self-learning capability, the neural networks obtained from NNs. To make this clearer, an illustrative (NNs) are wildly use in financial prediction problem. But the NNs example is also demonstrated in this study. More or less suffer from the slow convergence, "black-box" i.e., it is almost impossible to analysis them for how they work. The Fuzzy Neural Networks(FNN) allow to add rules to neural networks. This avoids the black-box but lacks of effective learning capability. To overcome these drawbacks, in this study a
allpro 2016-08-23
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Fluctuation of mean Lyapunov exponent for turbulen
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In recent years, a large number of investigations have been devoted to the complex Ginzburg Landau (CGL) equation. This equation has no potential and may be used to describe the complex behavior of physical systems. Those from oscillating chemical reactions on cardiac arrhythmias. Turbulence is one of them, which is one of the reasons why CGL equation has attracted many scientists. The turbulence includes stage turbulence and defect turbulence. In addition, the interaction vortex is introduced, and the spiral dilution gas is calculated based on the equation. The Belousov zabojingsky chemical reaction is a famous system. It is believed that it is one of the systems described by CGL equation
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Current transformer saturation compensation based
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This paper proposes a partial nonlinear model to accurately represent the nonlinear saturation characteristic of a current transformer (CT). Based on the model, the saturated section of the secondary current as well as the unsaturated section can be used in a regression process to estimate model parameters. The saturated section normally lies near the inception of a fault, therefore accurate parameters can be obtained faster compared with the methods using only unsaturated sections. The pre-fault remanent flux and DC-offset, which could significantly influence CT saturation, are both considered in the model, thus they do not affect the accuracy of the parameter estimation. The computational load of the regression calculation is significantly reduced by using separable nonlinear least squares (SNLLS) method. This provides the feasibility to implement the method for real-time protective relaying. The performance of the
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A Hybrid Multilevel Inverter Topology with Third Harmonic Injection for Grid Connected Photovoltaic
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A three phase hybrid multilevel inverter topology is proposed for grid integrated photovoltaic central inverter systems. This topology is a hybrid of cascaded H bridge topology (CHB) and neutral point clamped (NPC) topology. The inverter uses asymmetric structure of dc bus voltage to increase the number of levels of the inverter. Third harmonic injection is proposed in order to have better utilization of the dc bus and to obtain higher number of levels. Nearest voltage level control is adapted as modulation technique to have good dynamic performance with less computational burden. A 29-level MATLAB/Simulink model of the inverter is developed to verify the performance of the proposed topology. The simulation uses only two H-bridges per phase and one three phase NPC (threelevel) inverter to obtain 29 levels in line-to-line voltage. The objective of the work is: Reduction of number of switching devices, system (con
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