Neuro-fuzzy Inference System Controller Research Articles

Validation of photovoltaics powered UPQC using ANFIS controller in a standard microgrid test environment

The power quality improvement becomes one of the important tasks while using microgrid as main power supply. Because the microgrid is combination of renewable energy resources. The renewable energy resources are intermittent in power supply and at the peak loading condition it has to supply the required power. So, the power quality problems may increase in that time. Out of all power quality issues the voltage drop and harmonic distortion is considered as the most serious one. In recent years unified power quality conditioner (UPQC) is emerged as most promising device which compensates both utility as well as customer side power quality disturbances in effective way. The compensating potentiality used in the UPQC is limited by the use of DC link voltage regulation and the conventional proportional integral (PI) controller. In this paper the compensating potentiality of the UPQC device is controlled by an adaptive neuro fuzzy inference system (ANFIS) control and it is powered from the available photovoltaics (PV) power generation. The effect of adding an intelligent UPQC is tested in the standard IEEE-14bus environment. MATLAB 2017b is used here for testing and plotting the simulation results.

A New Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller to Control the Power System equipped by Wind Turbine

This study proposes a new Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to control the power system tuned by a wind turbine. The purpose of design is to improve the dynamical response of power systems after fault which the voltage controller has been improved. The effectiveness of the proposed approach is studied under different situations of three machines 9 bus power systems which the wind turbine is replaced by wind turbine equipped by ANFIS controller. The simulation results confirm that the tuning method is able to preserve optimal performances over wide range of disturbances. The results have demonstrated the high performances of the proposed technique in terms of low oscillation, ripple, rapidity and accuracy.

Simulation of PEM fuel cell integrated DVR using SRF-ANFIS controller for power quality enhancement

Voltage related Power Quality (PQ) problems are attracting the eyes of researchers, as these cause huge loss in productivity and profitability for both utilities and consumers. Dynamic Voltage Restorer (DVR) is a well-known custom power device that provides an economical solution for the alleviation of power quality problems. Generally, battery energy storage is used as an input for DVR, but batteries are still bulky and costly and must be disposed once their chemicals are used, which limits the compensation capability of DVR. Nowadays, Fuel Cell (FC) technologies have attracted much attention owing to their high efficiencies and low emissions. This project aims to model and simulate Proton Exchange Membrane Fuel Cell (PEMFC) and is used as a DC input source for DVR. The suitable boost converter is designed and modelled to lift up the fuel cell output voltage suitable as DC link voltage for DVR. Adaptive Neuro Fuzzy Inference System (ANFIS) controller uses Synchronous Reference Frame Theory (SRF) algorithm to generate d and q axis components of reference signal for Voltage Source Converter (VSC) of DVR. This PEMFC DVR is verified for balanced and unbalanced voltage sag, swell, and harmonics using MATLAB/SIMULINK. A simulation result proves that this PEMFC DVR is capable of providing a technically advanced and economic solution for balanced and unbalanced voltage sag and swell. During compensation, the DVR DC input voltage is also preserved as constant, which enhances the compensation capability of DVR.

Modeling and analysis: power injection model approach for high performance of electrical distribution networks

The generation of electrical energy varies depending on the needs of the user, initial requirements, capacity, intended use, waste generation, and economic efficiency. In order to meet the challenges of the proposed overvoltage of the presented system, it is possible to use the solar collectors and profit from them economically through smart grid smart control systems. The mathematical model with four main parts was created: simulation, correlation, and evaluation according to the solar program set of photovoltaic solar modules, maximum power point tracking (MPPT), an adaptive neuro-fuzzy inference system (ANFIS) controller, and 600-volt electric network. Then in this phase, the investigation of the effects on the network on the basis of the output power with the coincidence of radiation and the effect of temperature in the network is carried out. An analysis was carried out to evaluate the impact of these fundamental limitations in practical application. In this section, the simulation of the proposed system is discussed. The block diagram of the developed system is presented in the last part. The proposed system was assessed from the Matlab simulation tapes and graphs for each part of the system, and the results of the overall system simulation were taken into account.

Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller

Wind energy has been developed and is widely used as a clean and renewable form of energy. Among the existing variety of wind turbines, variable-speed variable-pitch wind turbines have become popular owing to their variable output power capability. In this study, a hybrid control strategy is proposed to implement pitch angle control. A new nonlinear hybrid control approach based on the Adaptive Neuro-Fuzzy Inference System and fuzzy logic control is proposed to regulate the pitch angle and maintain the captured mechanical energy at the rated value. In the controller, the reference value of the pitch angle is predicted by the Adaptive Neuro-Fuzzy Inference System according to the wind speed and the blade tip speed ratio. A proposed fuzzy logic controller provides feedback based on the captured power to modify the pitch angle in real time. The effectiveness of the proposed hybrid pitch angle control method was verified on a 5 MW offshore wind turbine under two different wind conditions using MATLAB/Simulink. The simulation results showed that fluctuations in rotor speed were dramatically mitigated, and the captured mechanical power was always near the rated value as compared with the performance when using the Adaptive Neuro-Fuzzy Inference System alone. The variation rate of power was 0.18% when the proposed controller was employed, whereas it was 2.93% when only an Adaptive Neuro-Fuzzy Inference System was used.

FPGA Realization of ANFIS Controller Using a Proposed Digital Design

The design and realization of Adaptive Neuro-Fuzzy Inference System (ANFIS) controller based on Field Programmable Gate Array (FPGA) is presented in this paper. The controller intended to control the temperature of medical oven. A novel design of digital ANFIS is presented here for the implementation process. Different controllers are designed and their results are compared using MATLAB program to show the ANFIS superiority. The designed controllers tested for cell cultures application at 37.5. A reduction is made for the designed digital ANFIS due to the used FPGA limitations. The reduced design minimizes the utilized slices from 366% to 3% and LUTs from 364% to 3%. The reduced design reached an optimum size for this controller to utilize a smallest memory size. A real-time FPGA implementation of the proposed digital ANFIS have been done and verified through Xilinx ISE 14.6 using the VHDL language. The VHDL code for the controller is produced, aggregated and downloaded on the FPGA Spartan 3A/AN FPGA kit. A comparison between the simulation and implementation results is made. The matching between these results proves the effectiveness and robustness of the proposed digital ANFIS and the excellent performance of the FPGA based controller.

Adaptive Neuro Fuzzy Technique for Speed Control of Six-Step Brushless DC Motor

The brushless DC motors with permanent magnets (PM-BLDC) are widely used in a miscellaneous of industrial applications. In this paper, The adaptive neuro fuzzy inference system (ANFIS) controller for Six-Step Brushless DC Motor Drive is introduced. The brushless DC motor’s dynamic characteristics such as torque , current , speed, , and inverter component voltages are showed and analysed using MATLAB simulation. The propotional-integral (PI) and fuzzy system controllers are developed., based on designer’s test and error process and experts. The experimential and hardware resuts for the inverter- driver circuits are presented. The simulation results using MATLAB simulink are conducted to validate the proposed (ANFIS) controller’s robustness and high performance relative to other controllers.

Adaptive Neuro Fuzzy Technique for Speed Control of Six-Step Brushless DC Motor

The brushless DC motors with permanent magnets (PM-BLDC) are widely used in a miscellaneous of industrial applications. In this paper, The adaptive neuro fuzzy inference system (ANFIS) controller for Six-Step Brushless DC Motor Drive is introduced. The brushless DC motor’s dynamic characteristics such as torque , current , speed, , and inverter component voltages are showed and analysed using MATLAB simulation. The propotional-integral (PI) and fuzzy system controllers are developed., based on designer’s test and error process and experts. The experimential and hardware resuts for the inverter- driver circuits are presented. The simulation results using MATLAB simulink are conducted to validate the proposed (ANFIS) controller’s robustness and high performance relative to other controllers .

Adaptive neuro fuzzy inference system based decoupled control for neutral point clamped multi level inverter fed induction motor drive

The presence of an integrator in a reference model of a rotor flux-based model reference adaptive system (RF-MRAS) and non-linearity of the inverter in the output voltage degrade the speed response of the sensorless operation of the electric drive system in terms of DC drift, initial value issues, and inaccurate voltage acquisition. To improve the speed response, a compensating voltage component is supplemented by an amending integrator. The compensating voltage is a coalition of drift and offset voltages, and reduces DC drift and initial value issues. During low-speed operation, inaccurate voltage acquisition distorts the stator voltage critically, and it becomes considerable when the stator voltage of the machine is low. Implementing a three-level neutral point clamped inverter in speed-sensorless decoupled control of an induction motor improves the performance of the drive with superior quality of inverter output voltage. Further, the performance of the induction motor drive is improved by replacing the proportional-integral (PI) controller in the adaption mechanism of RF-MRAS with an adaptive neuro-fuzzy inference system (ANFIS) controller. A prototype model of the three-level neutral point clamped inverter (3L-NPC)-fed induction motor drive is fabricated in a laboratory, and its performance for a RF-MRAS, modified RFMRAS, and modified RFMRAS using ANFIS are compared using different benchmark tests.

A control structure for ambidextrous robot arm based on Multiple Adaptive Neuro‐Fuzzy Inference System

This paper presents the novel design of an ambidextrous robot arm that offers double range of motion as compared to dexterous arms. The arm is unique in terms of design (ambidextrous feature), actuation (use of two different actuators simultaneously: Pneumatic Artificial Muscle (PAM) and Electric Motors)) and control (combined use of Proportional Integral Derivative (PID) with Neural Network (NN) and Multiple Adaptive Neuro-fuzzy Inference System (MANFIS) controller with selector block). In terms of ambidextrous robot arm control, a solution based on forward kinematic and inverse kinematic approach is presented, and results are verified using the derived equation in MATLAB. Since solving inverse kinematics analytically is difficult, Adaptive Neuro Fuzzy Inference system (ANFIS) is developed using ANFIS MATLAB toolbox. When generic ANFIS failed to produce satisfactory results due to ambidextrous feature of the arm, MANFIS with a selector block is proposed. The efficiency of the ambidextrous arm has been tested by comparing its performance with a conventional robot arm. The results obtained from experiments proved the efficiency of the ambidextrous arm when compared with conventional arm in terms of power consumption and stability.

Unified adaptive neuro-fuzzy inference system control for OFF board electric vehicle charger

This paper illustrates the control algorithm design for a two stage off board bidirectional smart electric vehicle (EV) charger architecture. The proposed EV charger is controlled to perform four quadrant operation (i.e. vehicle-to-grid (V2G) and grid-to-vehicle (G2V)) while compensating the load harmonics, simultaneously. Here, the 3-phase AC-DC converter and DC-DC converter are two main components, where the first one is controlled to regulate DC-link voltage as well as reactive power and current harmonics of nearby non-linear load while the second one regulates the exchange of active power. Generally, the AC-DC converter is controlled via two control loops, i.e. outer loop and inner loop. However, setting the gains of almost four proportional integral (PI) controllers and determining decoupling terms for inner control loops is very difficult, especially under the dynamic operating conditions. Therefore, an adaptive neuro-fuzzy inference system (ANFIS) has been designed to estimate the direct and quadrature axis reference currents directly while regulating two different quantities in single step only and hence, named as unified ANFIS controller. The proposed EV charger is simulated in MATLAB/Simulink and controller performance is validated with scaled down hardware model in real time.

A New Controller for Voltage and Stability Improvement of Multi Machine Power System Tuned by Wind Turbine

This paper proposes a new controller for stability and voltage improvement of power networks equipped by wind turbine which optimize the dynamical response of power systems performances (voltage and transient stability) after fault. The proposed control algorithm based on new Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to enhance the mechanical power of the synchronous machine into power system. The efficiency of developed control strategy has been tested using IEEE 9 Bus. Simulation results have showed that the proposed method perform better performances over wide range of disturbances for three considered scenarios studied.

Force tracking control for electrohydraulic servo system based on adaptive neuro-fuzzy inference system (ANFIS) controller

PurposeThe purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system (EHSS). Specifically, the adaptive neuro-fuzzy inference system (ANFIS) is selected to improve the control performance for EHSS.Design/methodology/approachTwo types of input–output data were chosen to train the ANFIS models. The inputs are the desired and actual forces, and the output is the current. The first type is to set a sinusoidal signal for the current to produce the actual driving force, and the desired force is chosen as same as the actual force. The other type is to give a sinusoidal signal for the desired force. Under the action of the PI controller, the actual force tracks the desired force, and the current is the output of the PI controller.FindingsThe models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller. The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot, rise time and mean absolute error and show adaptivity to different tracking conditions, including sinusoidal signal tracking and sudden change signal tracking.Originality/valueThis paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.

Voltage quality improvement in electrical distribution networks using dynamic voltage restorers: design, simulation and experimental tests of a robust controller

This paper presents a simulation and implementation of a proportional integral (PI) and adaptive neuro-fuzzy inference system (ANFIS) control scheme of single-phase dynamic voltage restorer (DVR) for the mitigation of load voltage sag, swell and harmonics. The objective of the control strategies is to regulate the voltage of the DVR via an injection transformer to compensate for the voltage required and maintain the load voltage at a constant value. However, the conventional compensation techniques are grouped only on the load voltage and the DVR itself. The aim is to regulate the injection voltage of the DVR to compensate the grid voltage via the injection transformer in addition to maintaining the load voltage stable. These methods have been adapted to sinusoidal references and is resistant to sags, swells and harmonics. The proposed control strategies of the DVR are initially evaluated in simulations under MATLAB/Simulink and then validated on a laboratorial prototype of the single-phase DVR. n in-depth analysis across different controllers would show the performance and their robustness in mitigating network power quality issues. The robustness of the proposed ANFIS controller is examined by comparative studies with the PI controller. We notice that the proposed ANFIS controller was able to obtain a lower total harmonic distortion than that of the PI method.

Virtual reality trajectory of modified PUMA 560 by hybrid intelligent controller

The high performance is a goal for all designers to get better, faster, or more efficient than others. This paper proposes a design for virtual reality (VR) of modified PUMA 560 by hybrid controller between adaptive neuro fuzzy inference system (ANFIS) controller and fractional order proportional, integral, derivative (FOPID) controller. The main purpose is to obtain the optimal trajectory by get the best value of controller’s parameters that regulate the manipulator movements smoothly to the desired target. The procedure of design start by obtains the optimal values of the traditional PID controller parameters normally. The next step is applied the FOPID controller with high accuracy. It is high performance to control the perplexing physics system than, the classical integer order of PID controller. The final step to get high performance of the control system under considers is achieved by hybrid between FOPID with ANFIS controller which used the pervious output as predictive point. The whole proposed hybrid controller model was simulated and reproduction by MATLAB Version 2019b and Robotic system Toolbox 9. The optimal design of this controller is applied with 3D model of modified PUMA 560 which design by using VR technique under MATLAB/Simulink.

Transient response improvement of direct current using supplementary control based on ANFIS for rectifier in HVDC

Current control scheme is commonly used in high voltage direct current (HVDC) to transmit power delivery. This scheme is done by adjusting trigger angle to regulate direct current (DC) in thyristor devices. The adaptive neuro-fuzzy inference system (ANFIS) control is widely applied for start and fault operation. But, solution for transient response of DC current in HVDC system is not clearly studied before. In this paper, supplementary control (SC) based on ANFIS is proposed to improve the transient response of the current. The SC control is designed by learning-processes and SC parameters are obtained by data-training automatically. For current reference at 1.05 pu and up-ramp at 20 pu/s, maximum overshoot is achieved at 5.12% and 7.72% for the SC and proportional integral controller (PIC), respectively. When the up-ramp is increased to 28 pu/s, the maximum overshoot is achieved at 10.01% for the SC. While, the peak overshoot for the PIC is 14.28%.

Design of an Executable ANFIS-based Control System to Improve the Attitude and Altitude Performances of a Quadcopter Drone

Nowadays, quadcopters are presented in many life applications which require the performance of automatic takeoff, trajectory tracking, and automatic landing. Thus, researchers are aiming to enhance the performance of these vehicles through low-cost sensing solutions and the design of executable and robust control techniques. Due to high nonlinearities, strong couplings and under-actuation, the control design process of a quadcopter is a rather challenging task. Therefore, the main objective of this work is demonstrated through two main aspects. The first is the design of an adaptive neuro-fuzzy inference system (ANFIS) controller to develop the attitude and altitude of a quadcopter. The second is to create a systematic framework for implementing flight controllers in embedded systems. A suitable model of the quadcopter is also developed by taking into account aerodynamics effects. To show the effectiveness of the ANFIS approach, the performance of a well-trained ANFIS controller is compared to a classical proportional-derivative (PD) controller and a properly tuned fuzzy logic controller. The controllers are compared and tested under several different flight conditions including the capability to reject external disturbances. In the first stage, performance evaluation takes place in a nonlinear simulation environment. Then, the ANFIS-based controllers alongside attitude and position estimators, and precision landing algorithms are implemented for executions in a real-time autopilot. In precision landing systems, an IR-camera is used to detect an IR-beacon on the ground for precise positioning. Several flight tests of a quadcopter are conducted for results validation. Both simulations and experiments demonstrated superior results for quadcopter stability in different flight scenarios.

Vibration Behaviour Control of a Fabricated One-Passenger Electric Vehicle with Either Mechanical or Air Suspension

Vehicle vibration transmission associated with the dynamic system depends on the frequency and direction of the input motion and the characteristics of vehicle suspension system and the seat from which the vibration exposure is received. A fabricated one-passenger electric vehicle equipped by a coil spring (mechanical) suspension system is introduced in this study. An air suspension system is used to replace the coil spring suspension system to improve ride comfort performance and intelligent classical adaptive neuro-fuzzy inference system controller is used to control the vehicle seat performance parameters. Accelerometers are mounted on the seat pan and seat base (floor) when measuring vertical acceleration. Data is frequently weighted according to standard BS 6841 in order to model the human response to vibration in terms of location and direction. The Simulink model is developed in Matlab software with the adaptive neuro-fuzzy inference system controller for the vehicle seat weighted vibration acceleration control. The results indicate that the predicted vibration acceleration can track the target vibration acceleration very well. Moreover, the values of the crest factor and kurtosis for the vehicle equipped by air suspension system are lower than those for the vehicle equipped by mechanical suspension system. Furthermore, the seat effective amplitude transmissibility for the fabricated vehicle with air suspension behaves lower value than that for mechanical suspension.

Neuro-fuzzy control of sit-to-stand motion using head position tracking

Based on the clinical evidence that head position measured by the multisensory system contributes to motion control, this study suggests a biomechanical human-central nervous system modeling and control framework for sit-to-stand motion synthesis. Motivated by the evidence for a task-oriented encoding of motion by the central nervous system, we propose a framework to synthesize and control sit-to-stand motion using only head position trajectory in the high-level-task-control environment. First, we design a generalized analytical framework comprising a human biomechanical model and an adaptive neuro-fuzzy inference system to emulate central nervous system. We introduce task-space training algorithm for adaptive neuro-fuzzy inference system training. The adaptive neuro-fuzzy inference system controller is optimized in the number of membership functions and training cycles to avoid over-fitting. Next, we develop custom human models based on anthropometric data of real subjects. Using the weighting coefficient method, we estimate body segment parameter. The subject-specific body segment parameter values are used (1) to scale human model for real subjects and (2) in task-space training to train custom adaptive neuro-fuzzy inference system controllers. To validate our modeling and control scheme, we perform extensive motion capture experiments of sit-to-stand transfer by real subjects. We compare the synthesized and experimental motions using kinematic analyses. Our analytical modeling-control scheme proves to be scalable to real subjects’ body segment parameter and the task-space training algorithm provides a means to customize adaptive neuro-fuzzy inference system efficiently. The customized adaptive neuro-fuzzy inference system gives 68%–98% improvement over general adaptive neuro-fuzzy inference system. This study has a broader scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level-task-control scheme.

Autonomous smart robot for path predicting and finding in maze based on fuzzy and neuro‐Fuzzy approaches

AbstractThe navigation of autonomous mobile robots has in recent times gained interest from many researchers in different areas such as the industrial, agricultural, and military sectors. This paper aims at carefully investigating two advanced types of approaches for guiding a non‐holonomic mobile robot to navigate in an environment area cluttered with static obstacles. Firstly, a Fuzzy logic controller (FLC) was designed, using trapezoidal shape Membership functions (MF's). Secondly, an Adaptive neuro fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy controller. To validate the feasibility and effectiveness of the proposed models, V‐REP and MATLAB software are used. A comparative evaluation is, then, done on the basis of speed. The simulations results showed that the mobile robot could navigate successfully into maze environment with both proposed approaches but ANFIS controller provided better results in comparison to fuzzy controller.