Adaptive Neuro-fuzzy Controller Research Articles

Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle

Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.

Proposed Fault Detection Algorithm with Optimized Hybrid Speed Control

The Brushless DC (BLDC) motor is a common choice for industrial applications, particularly in the automotive sector, owing to its high efficiency and robust capabilities. To detect the position of the motor rotor, hall-effect sensors can be used, but these sensors may prevent the system from operating if they fail. Consequently, fault-tolerant control (FTC) has been proposed in several studies to ensure continuity of operation in the event of sensor failure. This paper proposes an innovative method of fault detection in the hall effect sensor for a BLDC motor using combinatorial functions. This paper proposes an innovative method of hall-effect sensor fault detection for a BLDC motor using combinatorial functions. For the speed control of the BLDC under study, a hybrid adaptive neuro-fuzzy inference control (ANFIS) is implemented. In addition, the FTC signal reconstruction technique adopted has been improved to achieve motor start-up despite a fault in one of the sensors, thanks to well-defined fault detection algorithms. Simulation results are presented for each sensor failure case to test the effectiveness of the method used.

Adaptive TS-ANFIS neuro-fuzzy controller based single phase shunt active power filter to mitigate sensitive power quality issues in IoT devices

The emergence of Internet of Things (IoT) offers numerous functions, such as intelligent sensor integration, remote sensing, and high-speed data transmission, which have found widespread applications in the smart industry and commercial applications. The associative nonlinear effects of a variety of undesirable power quality concerns were resolved by using harmonic mitigation in nonlinear loads and high-performance converters were built on power electronics in conventional systems. Among other performance objectives, total harmonic (THD) distortion analysis and higher order harmonics mitigation is given main concern in smart electronics equipment. This paper proposes an approach to minimize higher-order harmonics due to nonlinear load disturbances in smart IoT devices using the Takagi–Sugeno (TS) Neuro Fuzzy (TS-ANFIS) supervised shunt APF. The proposed work elicits the novel adaptive harmonic mitigation technique in hybrid IoT embedded systems to protect from malfunctioning and to deal with the uncertainty of harmonic signal stimuli in sensitive sensors-based IoT systems. Hysteresis current control is used for the ignition of reference signal under uncertainty of harmonic stimuli. The single phase shunt APF is used to mitigate higher-order harmonics from supply mains, while the implementation of TS-ANFIS supervises the controller action to generate a trigger signal for adequate single phase APF gate excitation. The higher order harmonic current data set is used for error deviation for training neural networks and adaptive control. The estimated adoption of the neuron's empirical weight reduces the total THD to a significant reduction rate of 72.8 % to 0.78 %. The control mechanism is feasible for a range of smart IoT systems to adhere to the standard of 519 (IEEE).

Enhanced ship engine vibration reduction using magnetorheological dampers and Adaptive Neuro-Fuzzy Control System

ABSTRACT In this paper, an innovative approach is proposed to mitigate ship engine vibrations by integrating a semi-active control system with magnetorheological (MR) dampers and an Adaptive Neuro-Fuzzy Control System (ANFIS). The ANFIS controller adjusts MR damper damping force to effectively adapt to engine vibrations. A comprehensive mathematical model simulates the complex dynamics of the ship engine vibration system, validated with real-world data. The model incorporates engine vibration measurements and MR damper properties to train the ANFIS controller. Compared to passive control systems, the proposed semi-active system reduces torsional vibrations by 22.606% and longitudinal vibrations by 29.840%. The ANFIS controller dynamically adjusts damping force in response to engine conditions, ensuring optimal performance. This system effectively mitigates ship engine vibrations, improving overall ship performance and passenger comfort. Consistency between modeling and experimental results confirms the system's viability and efficacy.

Analysis of Grid-tied Solar Photovoltaic Energy Generation under Uncertain Atmospheric Conditions Using Adaptive Neuro-fuzzy Control System

The grid-tied photovoltaic (PV) power system has remained the most practical and sustainable configuration among renewable energy generation systems. Although uncertainties persist in solar irradiance and temperature, the grid-tied system faces transient instability issues during maximum power point tracking, adversely affecting power quality and resulting in substantial costs. To overcome this issue, we proposed analyzing the grid-tied system under uncertain atmospheric conditions based on an adaptive neuro-fuzzy control system (ANCS). This control scheme incorporates a hybrid learning algorithm and undergoes evaluation across various operating conditions. The obtained results demonstrate the effectiveness of the learning algorithm in maintaining a fast convergence speed. Consequently, this capability ensures the consistent preservation of sufficient power quality in the power system without any discernible transient impact. Furthermore, the investigation reveals the significant impact of solar radiation and temperature on the performance of the solar grid-tied PV system. Specifically, temperature alone contributes to over 15% power reduction when reaching 45 °C. As the temperature decreases to 5 °C at 1000 W/m2 irradiance, the ANCS influences an increase in the system's power generation from 100.72 kW at 25 °C to 103.01 kW.

Development of an Adaptive Fuzzy-Neural Controller for Temperature Control in a Brick Tunnel Kiln

This research focuses on developing an adaptive fuzzy-neural control system to manage and maintain temperature in brick tunnel kilns. The problem is how to optimize performance and energy consumption in the brick production process. A control algorithm using a combination of a fuzzy logic system and neural network is proposed to automatically adjust temperature parameters, optimize production efficiency, and reduce energy consumption. Simulation and experimental results demonstrate the outstanding performance of the presented system, with significant improvements in energy efficiency and product quality compared to traditional control methods. Moreover, the obtained results exhibit the potential for wide application of the adaptive neuro-fuzzy control method in academic study or industrial production processes.

Synthesis and Initialization of Adaptive Neuro-Fuzzy Control Based on the SDRE Technique for a Two-Link Manipulator

Synthesis and Initialization of Adaptive Neuro-Fuzzy Control Based on the SDRE Technique for a Two-Link Manipulator

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

Mitigation of power quality disturbances in wind energy conversion systems with fed unified power flow controllers using cascaded adaptive neuro-fuzzy inference system controller

A novel ANFIS-controlled customized UPQC device for power quality enhancement

Power quality is crucial for the reliable and efficient operation of domestic and industrial loads. Nonlinear power electronic converter loads have increased in usage, which has led to a decline in both voltage and current quality. To overcome these power quality issues, a universal power quality compensator (UPQC) has been developed and integrated into the power distribution network. The UPQC comprises dual active power filters which are equipped with a DC-link capacitor. The voltage of the DC-link capacitor is controlled by a DC-link voltage controller. However, the conventional PI controller is not suitable for regulating voltage at a defined level due to inappropriate gain values. In this work, we propose an intelligent adaptive neuro-fuzzy inference system-based DC voltage controller for customized UPQC devices. The proposed control scheme aims to mitigate the existing power quality challenges by reducing the issues in classical controllers and incorporating intelligence knowledge with subjective decisions. Using the MATLAB/Simulink software tool, we have tested a customized UPQC device controlled by ANFIS for critical operation and performance. The simulation results are presented along with valid comparisons.

Adaptive Neuro-Fuzzy Control of Active Vehicle Suspension Based on H2 and H∞ Synthesis

This paper addresses the issue of a road-type-adaptive control strategy aimed at enhancing suspension performance. H2 synthesis is employed for modeling road irregularities as impulses or white noise, minimizing the root mean square (RMS) of performance outputs for these specific road types. It should be noted, however, that this approach may lead to suboptimal performance when applied to other road profiles. In contrast, the H∞ controller is employed to minimize the RMS of performance outputs under worst-case road irregularities, taking a conservative stance that ensures robustness across all road profiles. To leverage the advantages of both controllers and achieve overall improved suspension performance, automatic switching between these controllers is recommended based on the identified road type. To implement this adaptive switching mechanism, manual switching is performed, gathering input–output data from the controllers. These data are subsequently employed for training an Adaptive Neuro-Fuzzy Inference System (ANFIS) network. This elegant approach contributes significantly to the optimization of suspension performance. The simulation results employing this novel ANFIS-based controller demonstrate substantial performance enhancements compared to both the H2 and H∞ controllers. Notably, the ANFIS-based controller exhibits a remarkable 62% improvement in vehicle body comfort and a significant 57% enhancement in ride safety compared to passive suspension, highlighting its potential for superior suspension performance across diverse road conditions.

Enhanced modular multi-level converter-based static synchronous compensator with social ski-driver optimized adaptive neuro-fuzzy inference system controller for power quality enhancement

Enhanced modular multi-level converter-based static synchronous compensator with social ski-driver optimized adaptive neuro-fuzzy inference system controller for power quality enhancement

Adaptive neuro fuzzy inference system-based energy management controller for optimal battery charge sustaining in biofuel powered non-plugin hybrid electric vehicle

In the hybrid electric vehicle, better performance can be acquired by implementing an optimized energy management strategy. The purpose of this work is to improve the efficiency of the energy management strategy for a biofuel-powered hybrid electric vehicle (HEV). The energy management system opted for this work is adaptive equivalent consumption minimization strategy (AECMS) with PI, fuzzy and adaptive neuro-fuzzy logic-based equivalence factor (EF) value adjustment. The appropriate selection of equivalence factor value in AECMS decides the optimal performance of an energy management. This entire work uses three standard combined driving cycles for training the intelligent controller and has been tested using a real-time driving cycle. The proposed adaptive neuro-fuzzy controller implemented in the AECMS energy management strategy provides the best results in terms of charging and discharging within defined operating limit of 40%-70% state of charge, delivers lower fuel economy and emissions compared to rule-based and fuzzy-PI AECMS. Adaptive neuro-fuzzy AECMS improves the fuel economy by 3% compared to 25.3 km/l of fuzzy-PI AECMS for the standard combined driving cycle (D1). Whereas for the D2 and D3 driving cycles 0.5–1% improvement has occurred. This work strives to prove that the proposed adaptive neuro-fuzzy AECMS controller is efficient and provide better fuel economy and lesser emissions.

Power quality enhancement of solar–wind grid connected system employing genetic-based ANFIS controller

Abstract The demand for electricity globally has led to the search for renewable energy resources for power generation and attaining it in an eco-friendly manner. The solar photovoltaic systems and wind-based generators of power are regarded as primary resources of renewable energy and are called Distributed Generation units as they are scattered in nature. These are operated with bidirectional converters by providing auxiliary services at grid side and load side in either mode of microgrid operation. Besides, the DC power generation units’ integration gets converted into AC system by means of inverters. These types of systems not only increase voltage and current harmonics and power frequency deviations but also drive the distribution system to risky operating zone. This emphasizes the stipulation of advanced control schemes for microgrid architecture. Consequently, power electronic converters introduce harmonics in the system and affect system performance. To report these expanded issues, the authors recognized an advanced custom power device entitled Distributed Power Flow Controller (DPFC). In this study, the proposed system on solar–wind-based hybrid energy approach has been examined primarily through the strategy of DPFC mechanism. Later, the system has been examined with Genetic Algorithm (GA)-based fuzzy logic controller and GA-based adaptive neuro fuzzy inference system controller for shunt control of context built with DPFC mechanism. Furthermore, the validated results are verified using MATLAB/Simulink software.

A Bee Colony Neuro-Fuzzy Controller to Improve Well Premixed Combustion

Abstract In order to actively control combustion reaction, this study proposes an adaptive neuro-fuzzy (ANFIS) control scheme of interaction between premixed combustion reaction and acoustic flame perturbation where the flame pressure movement will be considered as model perturbation. Using the Cantera database, it is possible to investigate the mechanisms by which the combustion process interacts with acoustic, vorticity, and entropy waves. A well-stirred reactor (WSR) has been extensively used to model combustion processes in three different reaction zone regimes. We designed the control architecture to achieve an intelligent representation of the system for various operating scenarios, which was motivated by the complexity of the mathematical model that was being used. This goal is accomplished by an artificial bee colony (ABC), which uses simulated data from a mathematical model to optimize a neuro-fuzzy with less computational expense. The optimized neuro-fuzzy identifier is converted to an adaptive neural-based (ANFIS) controller optimized to control the outputs of the system. In keeping with the combustion temperature set point, the results demonstrate a remarkable attenuation of flame perturbation and acceptable combustion reaction quality (NOx emission).

Solar Energy Dependent Supercapacitor System with ANFIS Controller for Auxiliary Load of Electric Vehicles

Innovations are required for electric vehicles (EVs) to be lighter and more energy efficient due to the range anxiety issue. This article introduces an intelligent control of an organic structure solar supercapacitor (OSSC) for EVs to meet electrical load demands with solar renewable energy. A carbon fibre-reinforced polymer, nano zinc oxide (ZnO), and copper oxide (CuO) fillers have been used in the development of OSSC prototypes. The organic solar cell, electrical circuits, converter, controller, circuit breaker switch, and batteries were all integrated for the modelling of OSSCs. A carbon fibre (CF)-reinforced CuO-doped polymer was utilised to improve the concentration of electrons. The negative electrodes of the CF were strengthened with nano ZnO epoxy to increase the mobility of electrons as an n-type semiconductor (energy band gap 3.2–3.4 eV) and subsequently increased to 3.5 eV by adding 6% π-carbon. The electrodes of the CF were strengthened with epoxy-filled nano-CuO as a p-type semiconductor to facilitate bore/positive charging. They improve the conductivity of the OSSC. The OSSC power storage was controlled by an adaptive neuro-fuzzy intelligent system controller to meet the load demand of EVs and auxiliary battery charging. Moreover, a fully charged OSSC (solar irradiance = 1000 W/m2) produced 561 W·h/m2 to meet the vehicle load demand with 45 A of auxiliary battery charging current. Therefore, the OSSC can save 15% in energy efficiency and contribute to emission control. The integration of an OSSC with an EV battery can minimise the weight and capacity of the battery by 7.5% and 10%, respectively.

Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid

The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day.

Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications

Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %.

A Novel Approach for an Outdoor Oyster Mushroom Cultivation using a Smart IoT-based Adaptive Neuro Fuzzy Controller

An automatic environment control systems for greenhouses are turning to be very significant because of food demand, and rise in temperature and population of the world. This article proposes to design and implement a low cost, robust and water efficient autonomous smart internet of things (IoT) system to monitor and control the temperature, and humidity of an outdoor oyster mushroom growing unit. The IoT-based control system involves DHT22 sensors, ESP32 controller and actuators (water pump and cooling fan) to facilitate the adequate amount of air for circulation to maintain temperature and water to maintain humidity inside an outdoor oyster mushroom growing unit as per its requirement. A real working prototype is developed and implemented on integrating fuzzy inference system (FIS) in ESP32 controller using Arduino C with the help of its integrated design environment. The FIS is designed to calculate the switching on/off time of water pump and cooling fan on sensing current temperature, and humidity inside oyster mushroom unit with respect to ambient temperature, and humidity respectively. The prototype provides inside temperature, humidity, ambient temperature, ambient humidity, water pump time and fan time on Thing-speak platform in real time. Furthermore, the data is used for design and simulation of Adaptive Neuro Fuzzy Inference Controller for an outdoor oyster mushroom growing unit in MATLAB/Simulink to improve the performance of the system. The practical applicability of the proposed ANFIS controller over FIS Controller and industrial PID Controller is shown by simulation findings with use of experimental data. The system reduces water use as well as an extremely extraordinary administration required for monitoring the mushroom unit. In addition, it increases robustness of the system.

Recent Advances for DC-DC Converter Topology in Hybrid Renewable Energy System

The hybrid renewable energy systems are widely employed to meet the load demand at various critical times. This paper proposes the modelling, simulation, and conversion of energy using multiple power electronic based DC-DC converter topologies in Hybrid Renewable Energy System (HRES) which consist of solar and wind turbine energy sources, for enhancing the system stability and efficiency. This work presents a novel high gain power electronic DC-DC known as Modified Single-Ended Primary-Inductor Converter with magnetic coupling for boosting voltage in HRES. Landsman converter is used to reach peak DC output voltage, improve power quality and voltage stability, reduce conversion of power stages, and decrease losses compared to the present power electronic converter coupled with HRES. Moreover, adaptive neuro fuzzy system controller is proposed in this research to gain peak power from photovoltaic system. The results are validated using SIMULINK/MATLAB software.

Synergy of neuro-fuzzy controller and tuna swarm algorithm for maximizing the overall efficiency of PEM fuel cells stack including dynamic performance

• A TSA-based methodology is established to optimize the PEMFC operating parameters for maximizing the efficiency. • Two optimization scenarios are presented to identify the parameters that most affect the PEMFCs stack efficiency. • The obtained accuracy of the TSA is proved against other competitors. • A neuro-fuzzy controller is designed to generate the operating parameters maintaining maximum efficiency irrespective the loading conditions. • The stack’s efficiency is improved at different loading conditions. For example, at 30% loading, the PEMFCs efficiency is improved from 16.27% to 63.47% at 60 °C and from 18.22% to 65.26% at 100 °C. Recently, world endeavors are focused on promoting energy savings by operating both sources and loads at their maximum efficiency points. Thus, this paper presents a novel attempt to optimally determine the operating parameters of an isolated system comprising the proton exchange membrane fuel cells (PEMFCs) stack serving a variable load. A fitness function is adapted to maximize the PEMFCs stack’s efficiency using tuna swarm algorithm (TSA), subjected to set of inequality constraints. A well-known commercial type of PEMFCs stack namely Nedstack PS6 6 kW, are carefully studied over two TSA-based optimization scenarios. The first scenario aims at optimizing five operating parameters, while only two operating parameters are optimized in the second one. Numerical comparisons among the two scenarios are made. It’s worth indicating that the maximum absolute efficiency deviation between both scenarios is equal to 0.8064 at 100 °C. Moreover, statistical tests are executed to appraise the performance of the TSA and others. At later stage, the TSA-based results are employed to train and learn an adaptive neuro-fuzzy controller for extracting the optimal operating parameters over wider range of loading conditions, while keeping the goal of maximum efficiency point in order. This allows predicting the optimal values of the operating parameters according to a certain load with a very low time burden, making it able to simulate the real-time load variations effectively and accurately. It can be reported here at low loading values as actual results for example, at 30% loading condition, the stack’s efficiency is improved from 16.27% to 63.47% at 60 °C, from 17.24% to 64.24% at 80 °C and from 18.22% to 65.26% at 100 °C. While, at load power of 40%, the FC’s efficiency is enhanced from 21.65% to 62.72% at 60 °C, from 22.95% to 63.60% at 80 °C and from 24.25% to 64.76% at 100 °C. It may be established that via this proposed synergy between TSA and neuro-fuzzy controller, the efficiency of PEMFCs can be maximized.