Elman Neural Network Controller Research Articles

Power management strategy based on Elman neural network for grid-connected photovoltaic-wind-battery hybrid system

In this study, an improved energy management controller (EMC) is proposed for a grid-connected hybrid system (HS), composed of wind–photovoltaic generation and an energy storage system (ESS). The main contributions of this study are as follows: first, we developed an intelligent supervisory controller based on a recurrent neural network, namely Elman neural network (ENN), to alleviate the complexity and requirement of a rule-based structure or prior mathematical modeling. Second, we designed an energy management strategy (EMS) using Stateflow (SF) approach to extract the training and testing datasets for construction of the ENN controller. The proposed framework was compared with a multilayer perceptron neural network (MLPNN) strategy to evaluate their performances. The simulation results indicated that the strategy proposed is a suitable option to efficient energy management control based on a predictive model, as it is not very complex and does not require a high processing machine.

Optimization Algorithm-Based Elman Neural Network Controller for Continuous Stirred Tank Reactor Process Model

Optimization Algorithm-Based Elman Neural Network Controller for Continuous Stirred Tank Reactor Process Model

Study on the Elman Neural Network Operation Control Strategy of the Central Air Conditioning Chilled Water System*

The stable operation of the central air conditioning water system always is a major difficulty for the control profession. Paper focus on the water system with multi variable, strong coupling, nonlinear, large time delay characteristics, presented use feed forward coupling compensation method, to eliminate the coupling effect between temperature and pressure. In this paper, the Elman neural network controller is designed for the first time, and the simulation results show that the response time of Elman neural network controller is shorter, the system is more stable and the overshoot is small.

Recurrent wavelet-based Elman neural network with modified gravitational search algorithm control for integrated offshore wind and wave power generation systems

A new approach to rotational speed control structures based on an optimized intelligent recurrent wavelet-based Elman neural network (RWENN) controller used for the integration of offshore wind and wave energy conversion systems driven by a doubly fed induction generator. The nodes connecting the weights of the RWENN are trained online using a backpropagation method. A modified gravitational search algorithm (MGSA) is developed to adjust the learning rates and improve learning capability. The proposed control scheme has improved the real power regulation and dynamic performance of a combined wind and ocean wave energy scheme over a wide range of operating conditions. The performance of this control scheme is assessed by comparing it to a traditional proportional-integral based control scheme in a series of case studies representative of maximum power generation. Simulations are carried out using PSCAD/EMTDC software to verify the robustness of the power electronics converters and the efficiency of the proposed controller under steady state and transient conditions.

A Cognitive System Design for Mobile Robot Based on an Intelligent Algorithm

This paper presents a cognitive system based on a nonlinear Multi-Input Multi- Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network (MENN) controller and the Square Road Map (SRM) method to guide the mobile robot during the continuous path-tracking with collision-free navigation through static obstacles. The proposed cognitive system consists of two parts: the first part is to plan the desired path for the mobile robot with the static obstacle environment in order to determine the target point and to avoid the obstacles based on the proposed square road map algorithm. The second part is to guide and track the wheeled mobile robot on the desired path equation based on the proposed nonlinear MIMO-PID-MENN controller with the intelligent algorithm. The Particle Swarm Optimization (PSO) is used to on-line tune the variable control parameters of the proposed controller to get the optimal torques actions for the mobile robot platform. Based on using the MATLAB package (2017), the numerical simulation results show that the proposed cognitive system has high accuracy for planning the desired path equation in terms of avoiding the static obstacles with smooth and short distance and generating a perfect torque action of (0.7 N.m) without a saturation state of (3.07 N.m), which leads to minimize the tracking pose error for the mobile robot to the zero value approximation. These results were confirmed by a comparative study with different nonlinear PID controller types in terms of number of iterations and the performance index.

Intelligent controlled shunt active power filter for voltage and current harmonic compensation in microgrid system

ABSTRACTAn integrated microgrid with a novel shunt active power filter (APF) using Elman neural network (ENN) is proposed in this study. The microgrid consists of a storage system, a photovoltaic (PV) system, the shunt APF, a linear load, and a nonlinear load. Moreover, the master/slave control algorithm is adopted in the microgrid. The storage system, which is considered as the master unit, is adopted to control the active and reactive power outputs (P/Q control) in grid-connected mode and the voltage and frequency of the microgrid (V/f control) in islanded mode. Furthermore, the PV system is considered as the slave unit to implement P/Q control in both grid-connected and islanded modes. In addition, the proposed shunt APF possesses dual functions of voltage and current harmonic compensation for microgrid under voltage harmonic propagation and nonlinear load to reduce the voltage and current total harmonic distortions (THD) effectively. Additionally, an ENN controller is adopted in the proposed shunt APF to improve the transient and steady-state responses of DC-link voltage during the switching between the grid-connected mode and islanded mode. Finally, some simulation results are provided to verify the feasibility and the effectiveness of the integrated microgrid with the intelligent controlled shunt APF.

Improved fault ride through capability of DFIG-wind turbines using customized dynamic voltage restorer

High penetration of wind power generation system into grid network has satisfied enormous energy demand and has necessitated the wind power generation system to remain connected with grid network to maintain essential grid stability, even during grid fault conditions. This paper presents a novel hybrid genetic algorithm optimized Elman neural network controller for proton exchange membrane fuel cell supported customized Dynamic Voltage Restorer (DVR) to improve the fault ride-through capability of Doubly Fed Induction Generator (DFIG) based wind power generation systems. The outstanding aspect of the proposed customized DVR is that it has low heat transmission losses, therefore has a better efficiency up to 3% when compared with the conventional energy storage based DVR. The proposed shrewd DVR adequately rides through the fault by improving voltage regulation and prevents wind turbine termination during grid faults. The effectiveness of the customized DVR and proposed control strategy is analyzed for doubly fed induction generator based wind power generation system under symmetrical and unsymmetrical fault conditions. The operation of customized DVR is confirmed using time-domain simulation study carried out in MATLAB/Simulink environment.

Improved differential evolution‐based Elman neural network controller for squirrel‐cage induction generator system

An improved differential evolution (IDE) algorithm-based Elman neural network (ENN) controller is proposed to control a squirrel-cage induction generator (SCIG) system for grid-connected wind power applications. First, the control characteristics of a wind turbine emulator are introduced. Then, an AC/DC converter and a DC/AC inverter are developed to convert the electric power generated by a three-phase SCIG to the grid. Moreover, the dynamic model of the SCIG system is derived for the control of the square of DC-link voltage according to the principle of power balance. Furthermore, in order to improve the transient and steady-state responses of the square of DC-link voltage of the SCIG system, an IDE-based ENN controller is proposed for the control of the SCIG system. In addition, the network structure and the online learning algorithm of the ENN are described in detail. Additionally, according to the different wind speed variations, a lookup table built offline by the dynamic model of the SCIG system using the IDE is provided for the optimisation of the learning rates of ENN. Finally, to verify the control performance, some experimental results are provided to verify the feasibility and the effectiveness of the proposed SCIG system for grid-connected wind power applications.

An improved elman neural network controller based on quasi‐ARX neural network for nonlinear systems

AbstractAn improved Elman neural network (IENN) controller with particle swarm optimization (PSO) is presented for nonlinear systems. The proposed controller is composed of a quasi‐ARX neural network (QARXNN) prediction model and a switching mechanism. The switching mechanism is used to guarantee that the prediction model works well. The primary controller is designed based on IENN using the backpropagation (BP) learning algorithm with PSO. PSO is used to adjust the learning rates in the BP process for improving the learning capability. The adaptive learning rates of the controller are investigated via the Lyapunov stability theorem. The proposed controller performance is verified through numerical simulation. The method is compared with the fuzzy switching and 0/1 switching methods to show its effectiveness in terms of stability, accuracy, and robustness. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Intelligent Control for USV Based on Improved Elman Neural Network with TSK Fuzzy

In recent years, based on the rising of global personal safety demand and human resource cost considerations, development of unmanned vehicles to replace manpower requirement to perform high-risk operations is increasing. In order to acquire useful resources under the marine environment, a large boat as an unmanned surface vehicle (USV) was implemented. The USV is equipped with automatic navigation features and a complete substitute artificial manipulation. This USV system for exploring the marine environment has more carrying capacity and that measurement system can also be self-designed through a modular approach in accordance with the needs for various types of environmental conditions. The investigation work becomes more flexible. A catamaran hull is adopted as automatic navigation test with CompactRIO embedded system. Through GPS and direction sensor we not only can know the current location of the boat, but also can calculate the distance with a predetermined position and the angle difference immediately. In this paper, the design of automatic navigation is calculated in accordance with improved Elman neural network (ENN) algorithms. Takagi-Sugeno-Kang (TSK) fuzzy and improved ENN control are applied to adjust required power and steering, which allows the hull to move straight forward to a predetermined target position. The route will be free from outside influence and realize automatic navigation purpose.

Real-Time Control Strategy of Elman Neural Network for the Parallel Hybrid Electric Vehicle

Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.

HYBRID MODIFIED ELMAN NN CONTROLLER DESIGN ON PERMANENT MAGNET SYNCHRONOUS MOTOR DRIVEN ELECTRIC SCOOTER

The electric scooter driven by permanent magnet synchronous motor (PMSM) has nonlinear and time-varying characteristic. An accurate dynamic model is not easy to establish for electric scooter in the linear controller design. In order to conquer the above problem, a novel hybrid modified Elman neural network (NN) control scheme is proposed to control for electric scooter driven by PMSM. The proposed control system consists of a supervisor control, a modified Elman NN and a compensated control with adaptive law. Finally, the effectiveness of the proposed novel hybrid modified Elman NN control system is demonstrated in comparison with the PI controller from some experimental results.

Hybrid Control of a Wind Induction Generator Based on Grey–Elman Neural Network

This brief presents the design of an optimal wind energy control system for maximum power point tracking. With the help of a grey predictor for the preprocessor, a high-performance online training Elman neural network (ENN) is designed to derive the turbine speed needed to extract maximum power from wind. Moreover, the connective weights of the improved ENN are trained online by the backpropagation learning algorithm. Compared to earlier methods, better results are obtained when the ENN controller is used together with the grey system modeling approach. Performance of the proposed approach is verified by the experimental results.

The Predictive Control Method of VAV Air Conditioning System

Aiming at the characteristics which variable air volume air conditioning system is multi-variable, nonlinear and uncertain system, normal fuzzy neural network is hard to meet the requirements which dynamic control of multi-variable. In this paper, we put forward a recursive neural network predictive control strategy based on wavelet neural network model. Through recursive wavelet neural network predictor on line established controlled object’s mathematical model, and using Elman neural network controller on line corrected information we get, thus to improve control effect. The simulation results show that recursive wavelet neural network predictive control has stronger robustness and adaptive ability, high control precision, better and reliable control effect and other advantages. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3244

Recurrent modified Elman neural network control of permanent magnet synchronous generator system based on wind turbine emulator

The two recurrent modified Elman neural networks (NNs) controlled permanent magnet (PM) synchronous generator system based on wind turbine emulator is proposed to regulate output voltages of rectifier and inverter in this study. First, the wind turbine emulator, which adopts a closed-loop PM synchronous servo motor drive system to act as prime machine, is designed to drive the PM synchronous generator system to yield the maximum power at different wind speeds. Then, the rotor speed of the PM synchronous generator, the DC bus voltage, and current of the rectifier are detected simultaneously to yield maximum DC power of the rectifier through DC bus power control. Moreover, one online training recurrent modified Elman NN controller is developed to regulate DC bus voltage in the output end of rectifier and another online training recurrent modified Elman NN controller is developed to regulate AC power in the output end of inverter in order to improve the control performance. Finally, some experimental results are verified to show the effectiveness of the proposed recurrent modified Elman NN controlled PM synchronous generator system based on wind turbine emulator.

Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive

The recurrent modified Elman neural network (NN) controlled a permanent magnet (PM) synchronous generator system, which is driven by wind turbine emulator of a PM synchronous motor servo drive, is developed to regulate output voltage of rectifier (or AC to DC power converter) and inverter (or DC to AC power converter) in this study. First, the wind turbine emulator of a closed loop PM synchronous motor servo drive is designed to produce the maximum power for the PM synchronous generator system. Then, the rotor speed of the PM synchronous generator, the output DC bus voltage and current of the rectifier are detected simultaneously to yield maximum power output of the rectifier through DC bus power control. Because the PM synchronous generator system is a nonlinear and time varying dynamic system, the online training recurrent modified Elman NN control system is developed to regulate DC bus voltage of the rectifier and AC line voltage of the inverter in order to improve the control performance. Furthermore, the online training recurrent modified Elman NN control system with the variable learning rate is derived based on Lyapunov stability theorem, so that the stability of the system can be guaranteed. Finally, some experimental results are verified to show the effectiveness of the proposed recurrent modified Elman NN controlled PM synchronous generator system.

PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River

Abstract The excavation face stability is crucial for safety and risk management in slurry shield tunneling, especially for the river-crossing tunnel. To avoid face collapse or blow-out, shield operators need to keep air chamber pressure balanced using their own experience, which would be difficult, discontinuous and less reliable in the process of construction. Considering the disadvantage of the manual control process, this paper presents a predictive control system for air chamber pressure in slurry shield tunneling using Elman neural network (ENN) model. It mainly contains a theoretical model, an ENN predictor and an ENN controller to set optimal control parameters automatically tracking the desired air chamber pressure. Moreover, to improve the learning capability of ENN model, a particle swarm optimization (PSO) algorithm is implemented. This system has been tested with collected data of slurry shield operation parameters in the Yangtze riverbed metro tunnel project in Wuhan, China. Analysis revealed that the predictive control system using PSO-based Elman neural network model in this paper has the potential for enhancing face stability in slurry shield tunneling.

Novel Modified Elman Neural Network Control for PMSG System Based on Wind Turbine Emulator

The novel modified Elman neural network (NN) controlled permanent magnet synchronous generator (PMSG) system, which is directly driven by a permanent magnet synchronous motor (PMSM) based on wind turbine emulator, is proposed to control output of rectifier (AC/DC power converter) and inverter (DC/AC power converter) in this study. First, a closed loop PMSM drive control based on wind turbine emulator is designed to generate power for the PMSG system according to different wind speeds. Then, the rotor speed of the PMSG, the voltage, and current of the power converter are detected simultaneously to yield better power output of the converter. Because the PMSG system is the nonlinear and time-varying system, two sets online trained modified Elman NN controllers are developed for the tracking controllers of DC bus power and AC power to improve output performance of rectifier and inverter. Finally, experimental results are verified to show the effectiveness of the proposed control scheme.

Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors

A novel recurrent wavelet-based Elman neural network (RWENN) control system is proposed in this study to control the mover position of a multi-axis motion control stage using linear ultrasonic motors (LUSMs) for the tracking of various contours. First, the structure and operating principles of the LUSMs are introduced briefly. Since the dynamic characteristics and motor parameters of the LUSMs are non-linear and time varying, the RWENN is proposed to control the mover of the X–Y–thetas motion control stage to track various contours precisely using a direct decentralised control strategy. In the proposed RWENN, each hidden neuron employs a different wavelet function as an activation function. Moreover, the recurrent connective weights are added in the RWENN. Therefore compared with the conventional Elman neural network (ENN), both the precision and time of convergence are improved. Furthermore, the on-line learning algorithm based on the supervised gradient descent method and the convergence analysis of the tracking error using a discrete-type Lyapunov function of the RWENN are developed. Finally, some experimental results of various contours tracking show that the tracking performance of the RWENN is significantly improved compared with the ENN.

FPGA-based elman neural network control system for linear ultrasonic motor

A field-programmable gate array (FPGA)-based Elman neural network (ENN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principle of the LUSM are introduced. Because the dynamic characteristics and motor parameters of the LUSM are nonlinear and time-varying, an ENN control system is designed to achieve precision position control. The network structure and online learning algorithm using delta adaptation law of the ENN are described in detail. Then, a piecewise continuous function is adopted to replace the sigmoid function in the hidden layer of the ENN to facilitate hardware implementation. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results.