Power Calculation Using RBF Neural Networks to Improve Power Sharing of Hierarchical Control Scheme in Multi-DER Microgrids

2016 ◽  
Vol 4 (4) ◽  
pp. 1217-1225 ◽  
Author(s):  
Hamid Reza Baghaee ◽  
Mojtaba Mirsalim ◽  
Gevork B. Gharehpetian
Keyword(s):  
Neural Networks ◽  
Hierarchical Control ◽  
Power Sharing ◽  
Power Calculation ◽  
Rbf Neural Networks ◽  
Control Scheme

Adaptive Feedback Linearization Control of SISO Nonlinear Processes Using a Self-Generating Neural Network-Based Approach

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Karim Salahshoor ◽  
Amin Sabet Kamalabady
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Feedback Linearization ◽  
Learning Algorithm ◽  
Rbf Neural Networks ◽  
Adaptive Feedback ◽  
Control Scheme ◽  
Feedback Linearization Control ◽  
Adaptive Control Scheme

This paper presents a new adaptive control scheme based on feedback linearization technique for single-input, single-output (SISO) processes with nonlinear time-varying dynamic characteristics. The proposed scheme utilizes a modified growing and pruning radial basis function (MGAP-RBF) neural network (NN) to adaptively identify two self-generating RBF neural networks for online realization of a well-known affine model structure. An extended Kalman filter (EKF) learning algorithm is developed for parameter adaptation of the MGAP-RBF neural networks. The MGAP-RBF growing and pruning criteria have been endeavored to enhance its performance for online dynamic model identification purposes. A stability analysis has been provided to ensure the asymptotic convergence of the proposed adaptive control scheme using Lyapunov criterion. Capabilities of the adaptive feedback linearization control scheme is evaluated on two nonlinear CSTR benchmark processes, demonstrating good performances for both set-point tracking and disturbance rejection objectives.

scholarly journals Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ruliang Wang ◽  
Jie Li
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Continuous Functions ◽  
Rbf Neural Networks ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Simulation Results

This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF) neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

scholarly journals Decentralized Fast Delayed Signal Cancelation Secondary Control for Low Voltage Ride-Through Application in Grid Supporting Grid Feeding Microgrid

2021 ◽  
Vol 9 ◽  
Author(s):  
Elutunji Buraimoh ◽  
Innocent E. Davidson ◽  
Fernando Martinez-Rodrigo
Keyword(s):  
Low Voltage ◽  
Hierarchical Control ◽  
Voltage Regulation ◽  
Power Sharing ◽  
Primary Control ◽  
Distributed Energy ◽  
Secondary Control ◽  
Low Voltage Ride Through ◽  
Control Scheme ◽  
Real Time Digital Simulator

In this study, a distributed secondary control is proposed alongside the conventional primary control to form a hierarchical control scheme for the Low Voltage Ride-Through (LVRT) control and applications in the inverter-based microgrid. The secondary control utilizes a fast Delayed Signal Cancelation (DSC) algorithm for the secondary control loop to control the reactive and active power reference by controlling the sequences generated. The microgrid consists of four Distributed Energy Resources (DER) sources interfaced to the grid through interfacing inverters coordinated by droop for effective power-sharing according to capacities. The droop also allows for grid supporting application for microgrid’s participation in frequency and voltage regulation in the main grid. The proposed decentralized fast DSC performance is evaluated with centralized secondary and traditional primary control using OPAL-RT Lab computation and MATLAB/SIMULINK graphical user interface for offline simulations and real-time digital simulator verification. This study presents and discusses the results.

A Safe Hierarchical Control Scheme Incorporated with Constrained Deep Neural Networks

2020 ◽  
Author(s):  
Hongyuan Wang ◽  
Jingcheng Wang ◽  
Haotian Xu ◽  
Shangwei Zhao ◽  
Xiaocheng Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Hierarchical Control ◽  
Control Scheme

Adaptive Control of Hyper-Chaotic Systems Based on Dynamic Structure RBF Neural Networks

2012 ◽  
Vol 229-231 ◽  
pp. 2311-2314
Author(s):  
Jing Wang ◽  
Hong Xia Gao ◽  
Zhen Yu Tan ◽  
Jin Feng Gao
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Chaotic Systems ◽  
Closed Loop ◽  
Dynamic Structure ◽  
Rbf Neural Networks ◽  
Closed Loop System ◽  
Control Scheme ◽  
The Stability ◽  
Adaptive Control Scheme

An adaptive control scheme based on neural networks is presented for control of hyper-chaotic systems. Parameters of neural networks and controllers are adjusted automatically to ensure the stability of the closed-loop system. Numerical simulation illustrates that the proposed control scheme is valid for hyper-chaotic system.

scholarly journals Nonlinear state feedback controller combined with RBF for nonlinear underactuated overhead crane system

2021 ◽  
Vol 9 (3A) ◽  
Author(s):  
Ali Hussien Mary ◽  
◽  
Abbas H. Miry ◽  
T. Kara ◽  
Mohammed H. Miry ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Control Method ◽  
Control Strategies ◽  
External Disturbance ◽  
Rbf Neural Networks ◽  
Overhead Crane ◽  
Control Scheme ◽  
System Uncertainties ◽  
Account System

This paper proposes a robust control scheme for an underactuated crane system. The presented scheme contains two control strategies, feedback control term and corrective control term, based on Radial Basis Function (RBF) neural networks. A feedback control term is deigned based on the nominal dynamic model of the controlled system. RBF neural networks have been used as adaptive control term to compensate for the system uncertainties and external disturbance. Lyapunov stability theorem has been used to derive updating laws for the weights of the RBF neural networks. To illustrate the robustness and effectiveness of the proposed controller, Matlab program is used to simulate the model of the nonlinear overhead crane system with the proposed control method, taking into account system uncertainties and external disturbance. Simulation results indicated superior control performance of the proposed control method compared to the other control methods used in the test.

scholarly journals Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA

2021 ◽  
Author(s):  
Volodymyr Shymkovych ◽  
Sergii Telenyk ◽  
Petro Kravets
Keyword(s):  
Neural Networks ◽  
Hardware Implementation ◽  
Gaussian Function ◽  
Activation Function ◽  
Rbf Neural Networks ◽  
Activation Functions ◽  
Rbf Network ◽  
Combination Scheme ◽  
Radial Basis ◽  
Hidden Layer

AbstractThis article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results of modeling of the Gaussian function on FPGA chips of different families have been presented. RBF neural networks of various topologies have been synthesized and investigated. The hardware component implemented by this algorithm is an RBF neural network with four neurons of the latent layer and one neuron with a sigmoid activation function on an FPGA using 16-bit numbers with a fixed point, which took 1193 logic matrix gate (LUTs—LookUpTable). Each hidden layer neuron of the RBF network is designed on an FPGA as a separate computing unit. The speed as a total delay of the combination scheme of the block RBF network was 101.579 ns. The implementation of the Gaussian activation functions of the hidden layer of the RBF network occupies 106 LUTs, and the speed of the Gaussian activation functions is 29.33 ns. The absolute error is ± 0.005. The Spartan 3 family of chips for modeling has been used to get these results. Modeling on chips of other series has been also introduced in the article. RBF neural networks of various topologies have been synthesized and investigated. Hardware implementation of RBF neural networks with such speed allows them to be used in real-time control systems for high-speed objects.

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