Neural Network Controlled High-Step Up Based Single-Phase PV Inverter System with Improved Dynamic Response

2017 ◽  
Vol 14 (1) ◽  
pp. 421-429
Author(s):  
M. L Bharathi ◽  
D Kirubakaran
Keyword(s):  
Neural Network ◽  
Dynamic Response ◽  
Single Phase ◽  
Closed Loop ◽  
Simulation Studies ◽  
Loop Control ◽  
Pv Inverter ◽  
Simulink Model ◽  
Neural Network Controller ◽  
The Neural Network

In AC utility closed loop high step up based single phase PV inverter system is preferred because of fast dynamic response. This paper deals with comparison of dynamic response of the PI and the neural network controlled single phase PV inverter systems. The DC output from the solar cell is stepped up using a high step up converter and the output is converted into AC using an inverter. A change in the insolation is considered in the present investigation. Simulation studies are conducted using MATLAB. The closed loop control systems using the PI controller and the neural network controller are investigated and their results are compared. The Simulink model for the above system are developed and they are successfully used for simulation studies. The hardware is fabricated, tested and the practical results matches with the simulation results.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

scholarly journals Fractional Order PID Controlled Cascaded Re-boost Seven Level Inverter Fed Induction Motor System with Enhanced Response

2018 ◽  
Vol 9 (4) ◽  
pp. 1784
Author(s):  
P. Bhaskara Prasad ◽  
M. Padma Lalitha ◽  
B. Sarvesh
Keyword(s):  
Dynamic Response ◽  
Induction Motor ◽  
Motor System ◽  
Single Phase ◽  
Closed Loop ◽  
Photovoltaic System ◽  
Voltage Response ◽  
Voltage Gain ◽  
Boost Regulator ◽  
Fractional Order Pid

<span lang="EN-US">Recently, Re-boost seven-level inverter has been developed as an alternative between Photovoltaic system and single-phase load. DC level is increased using a re-boost regulator and its output is rehabilitated into single-phase AC utilizing a seven-level inverter. The re-boost converter is utilized to escalate the voltage gain. The objective of the suggested closed loop Re-boost Seven Level Inverter fed Induction Motor (RBSLIIM) system is to enhance the dynamic response of RBSLIIM using FO-P-I-D controller. Simulink models are developed for P-I and FO-P-I-D controlled RBSLIIM systems. The results of P-I and FO-P-I-D based RBSLIIM systems indicate that the voltage response with FO-P-I-D is superior to P-I controlled RBSLIIM system.</span>

scholarly journals PI- Controlled PV Fed Fly Back Converter with Enhanced Dynamic Response

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4031-4034
Keyword(s):  
Steady State ◽  
Dynamic Response ◽  
Rise Time ◽  
Closed Loop ◽  
Input Voltage ◽  
Pi Controller ◽  
Open Loop ◽  
Load Variations ◽  
Simulink Model ◽  
Step Down

Fly back converter is the most popular converter because of its simplicity, low part counts and isolation. It occupies less volume and it saves cost. Fly back converter steps up and step down the voltage with the same polarity. Open loop operation remains insensitive to the input voltage and load variations. Matlab Simulink model for Fly back converter is established using PI controller. Open loop Fly back converter system and closed loop fly back converter systems are simulated and their outcomes are compared. Comparison is done in terms of Rise time ,Settling time and steady state error

Design and Realization of Experimental Autonomous Driving System Based on Neural Network Control

2012 ◽  
Vol 241-244 ◽  
pp. 1953-1958
Author(s):  
Qing Fu Kong ◽  
Fan Ming Zeng ◽  
Jie Chang Wu ◽  
Jia Ming Wu
Keyword(s):  
Neural Network ◽  
Autonomous Driving ◽  
Network Control ◽  
Neural Network Control ◽  
Test Results ◽  
Driving System ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Autonomous Driving System

Intelligent vehicle is an attractive solution to the traffic problems caused by automobiles. An experimental autonomous driving system based on a slot car set is designed and realized in the paper. With the application of a wireless camera equipped on the slot car, the track information is acquired and sent to the controlling computer. A backpropogation (BP) neural network controller is built to imitate the way of player’s thinking. After being trained, the neural network controller can give the proper voltage instructions to the direct current (DC) motor of the slot car according to different track conditions. Test results prove that the development of the autonomous driving system is successful.

Modeling and Simulation of Closed Loop Controlled Parallel Cascaded Buck Boost Converter Inverter Based Solar System

2015 ◽  
Vol 6 (3) ◽  
pp. 648 ◽  
Author(s):  
T. Sundar ◽  
S. Sankar
Keyword(s):  
Solar System ◽  
Modeling And Simulation ◽  
Single Phase ◽  
Closed Loop ◽  
Boost Converter ◽  
Open Loop ◽  
Boost Converters ◽  
Closed Loop Systems ◽  
Simulink Model ◽  
Simulation Results

<p>This Work deals with design, modeling and simulation of parallel cascaded buck boost converter inverter based closed loop controlled solar system. Two buck boost converters are cascaded in parallel to reduce the ripple in DC output. The DC from the solar cell is stepped up using boost converter. The output of the boost converter is converted to 50Hz AC using single phase full bridge inverter. The simulation results of open loop and closed loop systems are compared. This paper has presented a simulink model for closed loop controlled solar system.  Parallel cascaded buck boost converter is proposed for solar system.</p>

scholarly journals Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jiazhi Li ◽  
Weicun Zhang ◽  
Quanmin Zhu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
External Disturbance ◽  
Lyapunov Stability Theory ◽  
Robotic Manipulators ◽  
Adaptive Observer ◽  
Multiple Model ◽  
Neural Network Controller ◽  
The Neural Network ◽  
The Stability

This study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the parameters, different models of robotic are constructed. Then, the corresponding local neural network controller is constructed, in which the neural network has been used to approximate the uncertainty part of the control law, and an adaptive observer is implemented to estimate the true external disturbance. The WMNNAC strategy with improved weighting algorithm is adopted to ensure the tracking performance of the robotic manipulator system when parameters jump largely. Through the Lyapunov stability theory and the method of virtual equivalent system (VES), the stability of the closed-loop system is proved. Finally, the simulation results of a two-link manipulator verify the feasibility and efficiency of the proposed WMNNAC strategy.

scholarly journals Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santiago Rómoli ◽  
Mario Serrano ◽  
Francisco Rossomando ◽  
Jorge Vega ◽  
Oscar Ortiz ◽  
...  
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
Tracking Error ◽  
Closed Loop Control ◽  
Online Information ◽  
Fed Batch ◽  
Loop Control ◽  
Batch Bioreactor

The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

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