Adaptive Neuro-fuzzy Controller Research Articles

Redundancy resolution of a variable base frame of a 3-DoF cable-driven serial chain by using an adaptive neuro-fuzzy controller

This paper presents a novel approach using adaptive neuro-fuzzy techniques to design controllers for planar cable-driven serial chain robots with variable configurations. The approach consists of two key components: (1) deriving dynamic models for cable-driven serial chain robots which are independent of their structure, and (2) adaptively determining the optimal cable connection points. Traditional methods face challenges in obtaining accurate dynamic equations for cable-driven serial chain robots with high degrees-of-freedom, hence neural networks are employed to estimate the model. In order to handle the variability in cable connection points, adaptive fuzzy methods are utilized. The proposed adaptive neuro-fuzzy controller algorithm introduces two new indices, namely cost-of-redundancy and degree-of-redundancy, to effectively address redundancy concerns. Additionally, the algorithm efficiently reduces the search space for finding the optimal configuration. Simulation results for a planar 3 degrees-of-freedom cable-driven serial chain robot using this algorithm showcase a noteworthy 42% reduction in cost-of-redundancy and an impressive 53.125% reduction in search space.

Retraction Note to: Design and performance analysis of adaptive neuro-fuzzy controller for speed control of permanent magnet synchronous motor drive

Retraction Note to: Design and performance analysis of adaptive neuro-fuzzy controller for speed control of permanent magnet synchronous motor drive

Adaptive neuro-fuzzy inference control for active stabilizer bars based on multiple data sources.

In this research, we propose using active stabilizer bars to prevent rollover when steering. An intelligent control solution, the adaptive neuro-fuzzy inference system (ANFIS), is established in this article to control the performance of the active anti-roll systems. In contrast to the previously published studies, the intelligent algorithm designed in this research has many outstanding advantages, such as generating a large impact force, a fast response time, a small phase difference, and high convergence ability. The data used to train ANFIS are carefully selected and combined from the previous studies. The initial simulation observes that the roll angle decreases significantly from 8.15° to 6.87° when the ANFIS algorithm is applied to regulate the anti-roll bars. In contrast, the roll angles for the proportional-integral-derivative (PID) and passive (Mechanical) situations are respectively recorded at 7.08° and 7.80°. The reduction of the vertical force at wheels is also solved when the ANFIS algorithm is applied instead of other methods. This value increases sharply from 671.06 N (without bars) to 3030.40 N (ANFIS control), while it only reaches 2544.27 N (PID control) and 1428.83 N (mechanical bars), according to the research findings. If the vehicle does not have the anti-roll bars, a rollover occurs in the second case when the vehicle steers at v3 (80 km/h) and v4 (90 km/h). In contrast, the interaction between the wheels and the road is well maintained when the automobile is equipped with active bars controlled by the ANFIS solution. This is demonstrated by the minimum vertical force value in the rear wheel, which reaches 2687.33 and 2447.33 N, respectively, for the abovementioned conditions. In general, the vehicle's rolling stability can be well guaranteed in all moving situations when using the ANFIS controller for the anti-roll system in the vehicle.

Novel MLI-based DVR and DSTATCOM with ANFIS control for enhanced power quality improvement

This paper proposed a novel multi-level inverter and a hybrid renewable energy source integrated dynamic voltage restorer and static synchronous compensator to reduce grid power quality issues. The 51-level asymmetrical multi-level inverter is proposed in this work with less number of switches and DC sources. Hybrid renewable energy sources like solar and wind are used to feed input power to multi-level inverters. In addition, the battery is connected to the hybrid renewable energy sources to store the excess energy and deliver. After that, the 51-level asymmetrical multi-level inverter is connected to improve the power quality. The switching pulses for the proposed multi-level inverter are generated by including the pelican optimization algorithm optimized adaptive neuro-fuzzy interference system controller. This proposed method is implemented in the Matlab/Simulink tool to validate the proposed method. The results show that the proposed inverter optimally achieved 51 levels, and the proposed method improved the power quality. The results confirmed that the proposed inverter had a total harmonic distortion of 1.41 % and 1.23 % with dynamic voltage restorer and distributed static synchronous compensator connection, which is lower than the existing 51-level inverter topologies.

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.

Design of Fast Real Time Controller for the SSSC Based on Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Control System

FACTS (Flexible AC Transmission Systems) technology is one aspect of the power electronics revolution that is taking place in all areas of electric energy. The power transfer capability of a transmission line can be enhanced by using power electronics based system like FACTS that offer high speed and reliable control capability. The Static Synchronous Series Compensator (SSSC) is the second generation of the FACTS controller, which is used to control the power flow in the transmission line. In this paper, a new scheme to control SSSC using adaptive neuro fuzzy logic is proposed, in this controller, Takagi-Sugeno fuzzy rules are trained using off-line neuro fuzzy system, also, instantaneous power theory is used to calculate the power system parameters due to its high accuracy and less computation. The simulation and practical results show that real time application of the proposed controller is possible and robust compared to conventional controllers previously investigated. The experiment results obtained using the dSPACE data acquisition system and Matlab real time toolbox.

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.