Adaptive Fuzzy Logic Controller Research Articles

The LLCL filter-based synchronverter with adaptive fuzzy logic controller

Global warming has emerged as a significant issue, primarily due to the use of fossil fuels for power generation. Renewable energy adoption has surged. However, stability issues arise when integrating the primary source and inertia-less nature of converter-interfaced renewable energy sources (RESs) into the traditional power system, due to its intermittent and uncertain nature. The proposed solution involves controlling converter-interfaced RES to emulate the characteristics of a conventional synchronous generator (SG), commonly known as a virtual synchronous generator or synchronverter. As it fundamentally functions as a power converter, it is crucial to consider the appropriate filter design for the synchronverter. Additionally, by emulating the characteristics of SG, the values of “virtual” inertia and droop coefficients must be chosen accurately to provide a robust response under different operating scenarios of the synchronverter. Hence, this paper proposes a novel LLCL filter-based synchronverter system along with an adaptive control strategy using a fuzzy logic controller (FLC). The system is simulated under different operating scenarios in MATLAB/Simulink. The proposed system demonstrated improved frequency response and reduced total harmonic distortion compared to the conventional synchronverter model for all the different operating scenarios, thus enhancing grid stability and power quality.

Enhanced Hybrid Power Grid System Using Adaptive Fuzzy Logic Controller by Supraharmonics Reduction

Hybrid Systems meet the growing needs of electrical energy in the countries. The mismatching of hybrid systems leads to the phase shift in the three-phase signals, producing more harmonics in the band of 2 to 150kHz.However, due to various factors, harmonics have arisen from the RITHAPF and TCR which act as the phase shift controller in hybrid systems. Therefore, the Adaptive Fuzzy controller is employed with RITHAPF and TCR to maintain the phase shift and attenuate the harmonics produced. which outperforms the Hysteresis controller by 17.5% approx. in harmonic suppression and the simulation carried out for various phase angles.

A new adaptive fuzzy logic control for nonlinear car active suspension systems based on the time-delay

A new adaptive fuzzy logic control for nonlinear car active suspension systems based on the time-delay

A new single switch universal supply voltage DC-DC converter for PV systems with MGWM-AFLC MPPT controller.

The present power generation government companies focus on Renewable Power Sources (RPS) because their features are zero carbon footprint, unlimited power source, fewer greenhouse pollutants, fewer output wastages, plus creatinga very healthy atmosphere. In this work, the sunlight source is utilized for the Photovoltaic (PV) standalone network. The merits of sunlight sources are very optimal human resources needed, unlimited natural sources, plus easy operation. However, the solar power resource is nonlinear fashion. As a result, the operating point of the sunlight network fluctuates concerning sunlight intensity. So, in this article, the Modified Grey Wolf Methodology with Adaptive Fuzzy Logic Controller (MGWM-AFLC) is introduced to maintain the operating point of the sunlight system at the global power point position of the PV array. This controller traces the MPP with very low fluctuations in the PV-produced voltage. The advantages of this proposed method arefewer sensing devices required, less difficulty in development, more useful for rapid changes inthe sunlight temperatures, simpler to realize operation, greater economic growth, plus highly useful for household applications. The sunlight set-up generation voltage is lowwhich is improved by introducing the new Wide Power Rating High Voltage DC-DC Boost Converter (WPRHVBC). The features of this WPRHV converter are low voltage strain on semiconductor devices, few passive elements are enough to develop the circuit, plus easy understanding.

A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions

Renewable energy resources are more useful when associated with the thermal power generation network because of their high accessibility in the environment, good system response, easy manufacturing, plus high scalable. So, the present research is going on solar power to reduce consumer grid dependency. The running of the PV network is quite easier, plus less human sources are involved. However, the solar modules’ power generation is nonlinear fashion. So, the collection of peak power from the sunlight-dependent systems is a highly challenging task. In this article, a Modified Differential Step Grey Wolf Optimization with Adaptive Fuzzy Logic Controller (MDSGWO with FLC) is developed for collecting the maximum power from renewable energy resources under diverse Partial Shading Conditions (PSCs). The introduced method comprehensive analysis has been done along with the other recently existing MPPT methods in terms of convergence speed, MPP tracking accuracy, operating efficiency of the introduced method, functioning duty value of the DC–DC boost power converter, dependence of MPPT on sunlight system, total number of sensing devices are needed, plus peak power extraction from the proposed system. Here, the sunlight power generation cost is more to limit this issue, a power converter is selected in the second objective to develop the voltage source capability of the PV network. The overall PV-interfaced power converter network is examined by utilizing the MATLAB environment.

Implementation of modified P&O and an adaptive fuzzy logic controller based MPPT tracking system under partial shading and variable environmental conditions

Most of the conventional Maximum Power Point Tracking (MPPT) Algorithms provide the finest efficiency during uniform irradiation conditions, but under variable irradiation or partial shading condition (PSC), the performance deteriorates and the solar PV system is unable to provide the maximum electricity out of the PV Modules. This degradation in performance occurs due to the presence of numerous local maximum power points (LMPP) and a single global maximum power point (GMPP) in the power versus voltage (P-V) characteristics and the perfect tracking of these LMPP is not possible with the prevailing MPPT algorithms. In order to eradicate this shortcoming, we have proposed the implementation of an adaptive Fuzzy Logic Controller (FLC) based on Perturb and Observe (P&O) technique by employing a boost converter with variable resistive load under uniform irradiance condition (UIC), dynamic atmospheric conditions (DAC) and PSC in this article. We also demonstrated the usage of a simple and improved P&O algorithm by employing a buck boost converter for faster tracking time. These two suggested approaches of FLC and P&O incorporate a variable load scenario and a stable load scenario respectively to address the problem of output voltage oscillation in a PV-based system. The FLC is designed to adjust the dynamic change in step size based on the rate of change of the yield power of the PV panel to reduce oscillations in output of the boost converter and the PV system. According to the results of this analysis, the recommended FLC-based P&O (FLC-P&O) algorithm outperforms the proposed P&O algorithm under DAC and PSC with respect to tracking speed, steady-state error, and dynamic response. Apparently, it can be concluded that the proposed FLC-P&O technique can be applicable to real-time systems for reliable and efficient operation of the boost converter and add to the stability and simplicity of the contemporary PV-based systems.

Performance Analysis of ANFIS-PID Controller based Speed Regulation and Harmonic Reduction in BLDC Motor Application

This study focuses on assessing the performance of a Proportional-Integral-Derivative (PID) controller integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS) in the context of speed regulation and harmonic reduction in Brushless DC (BLDC) motor applications. Rising BLDC motor speed elevates Total harmonic distortion (THD) due to non-linearity. THD reduction is vital for efficiency, reliability, and compliance in applications like electric vehicles, HVAC, and industrial automation, ensuring optimal performance and longevity. Through simulation-based design and implementation, the effectiveness of the ANFIS-PID controller is evaluated for achieving precise speed control and reducing harmonic distortions in a virtual environment. Various conventional control topologies are considered, with the ANFIS-PID controller demonstrating superior performance. The synergy of adaptive fuzzy logic and classic control components allows the ANFIS-PID controller to outperform others, particularly in dynamic conditions and varying motor characteristics, offering enhanced speed regulation and harmonic reduction in BLDC motor applications. Detailed simulations in MATLAB/Simulink software thoroughly assess the controller's dynamic response and its ability to accurately regulate BLDC motor speed while concurrently reducing harmonic distortions.

Trajectory tracking control of autonomous space-based simulators for the on-orbit assembly of large space optical telescopes

To enhance the precision of on-orbit assembly for large space optical telescopes, the control of attitude motion in the Autonomous Space-based Simulator (ASS) is imperative. This paper presents a robust finite-time trajectory tracking control method aimed at improving the position and attitude tracking performance of ASS. Considering the dynamic characteristics inherent in ASS, we introduce an improved non-singular fast terminal sliding mode controller (INFTSMC) incorporating an adaptive fuzzy mechanism to attain finite-time error convergence and robust control. The adaptive fuzzy logic control (AFLC) method yields an equivalent gain, eliminating discontinuous switching in terminal sliding mode control (TSMC). This reduction minimizes chattering in control inputs and compensates for uncertainties and time-varying disturbances within the dynamic model. The proposed method’s effectiveness is validated through comprehensive simulation and experimental studies.

Experimental validation of double-fed induction generator in oscillating water column using circulatory system-based optimization

Abstract This paper validates the optimal operation for a grid-connected double-fed induction generator (DFIG) in an oscillating water column power plant (OWCPP). This study presents a novel optimization technique called the circulatory system-based optimization (CSBO) approach to develop six adaptive fuzzy logic controllers (AFLCs) with 30 parameters and compare them to chaotic-billiards optimization (C-BO) and genetic algorithm (GA). The proposed controller is also compared with a proportional–integral differential (PID) controller based on a self-adaptive global-best harmony search (SGHS). CSBO-based AFLCs are fully investigated under different scenarios and experimented with using a real-time interface DSP1104. The results of using CSBO–AFLCs revealed a fast time response, fast convergence, less overshoot and minimal error compared with those achieved with C-BO–AFLC, SGHS–PID and GA–AFLC during different case studies. The CSBO-based AFLCs ensure maximum power from the DFIG in an OWCPP and enhance dynamic response with very low errors. The results show that the CSBO shows better power tracking by 25% as compared with C-BO, by 45% when compared with the GA and by 56% when compared with PID. Moreover, the integral absolute errors of six controllers are investigated to demonstrate the feasibility of CSBO–AFLC. The root mean square of the errors of six controllers using CSBO is improved by 68.27% when compared with GA, by 22.57% when compared with C-BO and by 38.42% when compared with PID. These indicators demonstrate the feasibility of CSBO when compared with other algorithms with the same OWCPP.

Artificial Intelligence Techniques based on aquaculture solar thermal water heating system control

The One of the most promising applications of hot water is aquaculture. Temperature is one of the most principle parameters affects aquaculture life. It can cause stress and mortality or superior environment for growth and reproduction. Shrimp is warm water aquaculture type need hot water supply. To increase pond production it is necessary to keep water temperature at 34oC. Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. In this paper a control system using artificial neural network control (NNC) either adaptive fuzzy logic control (AFLC) are design to control the hot water temperature. A comparison study is applied between the performance of FLC and NNC. The performance of NNC is the best because the control design takes into consideration different variables which give an accurate output than FLC. Also this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the proposed aquaculture heating system.

An Adaptive Fuzzy Logic Control Method for Froth Velocity in the Flotation Process

In the flotation process, too high or too low froth velocity can lead to a reduction in the recovery of useful minerals. However, it is difficult to achieve stable control of froth velocity due to complex process characteristics and fluctuating working conditions. Uncertain feed conditions (feed flow rate, feed grade, and feed concentration) can cause large changes in the desired froth velocity and other uncertain conditions, which affect the accurate evaluation of the velocity state for feedback control. In this paper, an adaptive fuzzy logic control method for froth velocity (AFLC-FV) in the flotation process is proposed. In the method, an adaptive fuzzy quantization function with a dead zone is designed and used instead of the constant fuzzy quantization factor in the fuzzification procedure. Thus, the dynamic real domain of the velocity error can be mapped to a fixed standard fuzzy domain. Meanwhile, the width of the dead zone is adaptively adjusted according to froth mobility to improve steady-state performance. In addition, the velocity trend is extracted and used as an input variable to the controller together with the velocity error. The application results show that, compared with manual control, the AFLC-FV significantly enhances the control accuracy and stability under different ranges of velocity set-points, and effectively improves the mineral recovery by 0.18%.

Detection of turn short circuit fault in PMSM variable speed sensorless vector control based on luenberger observer and adaptive Fuzzy Logic Controller

Detection of turn short circuit fault in PMSM variable speed sensorless vector control based on luenberger observer and adaptive Fuzzy Logic Controller

A novel on high voltage conversion ratio DC‐DC converter for EV‐fed fuel cell application with hybrid MPPT controller

SummaryAs of now, nonrenewable energy sources are not used for automotive industrial applications because their disadvantages are high installation costs, more maintenance costs, and less adaptability all over the places in the world. Also, these sources produce highly flammable gasses in the environment. So, in this work, a Proton Exchange Membrane Fuel Stack (PEMFS) is used as an input source for the proposed system. The PEMFS generated voltage value is very low which is improved by integrating the proposed Single Switch High Voltage Conversion Ratio (SSHVCR) DC‐DC converter. The properties of this SSHVCR converter are less component utilization for reducing the ripple ratio, more flexibility, less voltage stress, and high voltage conversion ratio. In the second objective, a Differential Step‐Grey Wolf Optimization (DSGWO) with Adaptive Fuzzy Logic (AFL) controller is used for generating switching pulses to the DC‐DC converter. The merits of DSGWO‐AFL controller are quick dynamic response, moderate design complexity, good adaptability, and needed few number iterations for identifying the MPP of PEMFS. The overall proposed system is studied by applying the MATLAB/Simulink tool at rapid changes in the operating temperature of the fuel cell stack.

Design and implementation of hybrid MPPT controller for FC based EV system with boost DC-DC converter

The fuel cell-dependent electric vehicle systems are giving an important role in the present automotive systems because their features are less air pollution, high flexibility, reduced oil dependency, and more reliability. However, the fuel stack delivers nonlinear output V-I characteristics. So, the extraction of peak power from the fuel source is very difficult. In this work, a Variable Step Size Radial Basis Functional Network-based Adaptive Fuzzy Logic Controller (VSSDE-AFLC) is proposed for tracking the peak power point of the fuel cell system. The merits of the proposed Maximum Power Point Tracking (MPPT) controller are high tracing speed of functioning point of the fuel cell, more flexibility, high abundant, acceptable oscillations across MPP, and less dependency on modeling of the fuel stack. Also, the single switch converter is utilized for increasing the voltage supply of the fuel cell. The features of the proposed converter are wide input operation, less voltage stress, high supply voltage conversion ratio, and good dynamic response. The proposed fuel cell-dependent boost converter is implemented by utilizing the MATLAB/Simulink software, and the converter is tested successfully by using the desired programmable DC supply.

Maximum power point tracking for grid-connected photovoltaic system using Adaptive Fuzzy Logic Controller

Photovoltaic (PV)-based energy solutions are utilized around the world as the demand for energy from renewable energy sources continues to increase. The advantages of a renewable energy source (RES)-based PV system are that it is environmentally friendly and needs less maintenance. The provision of consistent voltage is necessary for PV systems that are connected to the grid. The sun's irradiation varies often during the day. The availability of grid-connected PV systems can be increased by implementing an Adaptive Fuzzy Logic Controller (AFLC) with nonlinear reactive power and a DC-Link voltage controller. To solve the issue of resilience against uncertainties and outside disruptions, a state-variable control strategy is adopted. The controller uses maximum power point tracking (MPPT) to ensure that the utility system receives the most electricity possible in situations with fluctuating solar radiation. Results from the proportional-integral (PI) and Fuzzy Logic Controllers are compared to the collected results (FLC).

Modelling and an adaptive fuzzy logic controller of solar thermal power plant

Modelling and an adaptive fuzzy logic controller of solar thermal power plant

Adaptive Fuzzy Logic-Based Control and Management of Photovoltaic Systems with Battery Storage

Renewable energy sources (RESs) such as solar photovoltaic (PV) systems are increasingly used as distributed generation for replacing the conventional energy. At the same time, energy storage systems like battery (BAT) must be applied for maintaining the balance between fluctuating energy production and load consumption. BAT’s state of charge (SOC) should be maintained within their design limits unaffected by RES intermittency and/or load power variations. This necessitates advanced power control and management methodologies for overcoming challenging conditions. This paper discusses and evaluates an optimal DC bus voltage regulation approach: an intelligent controller using an adaptive fuzzy logic controller (FLC) and a novel supervisory power management strategy for PV systems with BAT. The objectives are to keep a stable power flow in the system and guarantee the continuity of service by ensuring that the system components do not exceed their limits. In this manner, the DC bus voltage regulation of the PV/BAT system can be improved in comparison with conventional regulation. Therefore, the most important contributions of this work are as follows. (1) Development of comprehensive and modular novel energy management system (EMS): its originality is related to the inclusion of the control system limits with faster SOC balancing and smaller DC bus voltage fluctuation. (2) Providing a simple power flow management implementation that considers the optimal energy flow between PV system, BAT system, and load: a balance between minimal energy flow in the connecting line and the least requirements of BAT capacity is kept, reducing component constraints with a very straightforward structure. (3) Furthermore, FLC offers high robustness and smooth performances. FLC is added to the control strategy design requirements to reduce DC bus voltage deviation. (4) Real-time simulation/experimentation-based complete cases utilizing Matlab/Simulink and DSpace are illustrated to testify the effectiveness of the proposed FLC and EMS.

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance.

An adaptive fuzzy logic control technique for LVRT enhancement of a grid-integrated DFIG-based wind energy conversion system

The fast improvement of wind energy conversion technology with optimization algorithms has recently received a lot of attention. However, their slow convergence speed, huge computational load, and low efficiency are the main drawbacks and to improve these disadvantages, a new adaptive fuzzy logic controller strategy is proposed to enhance the low voltage ride-through of the grid-connected doubly fed induction generator during severe grid faults. The rotor side converter and the grid side converter are controlled using an adaptive fuzzy logic controller topology under cascaded structure to improve the performance. The novel application of the ‘generalized variable step-size diffusion continuous mixed p-norm’ adaptive filtering algorithm is proposed to modify the calibrating factors of the fuzzy logic controllers at a rapid convergence speed with low normalized misalignment error. The proposed adaptive algorithm-based fuzzy logic controller has a better low voltage ride-through improvement capability than that of using the particle swarm optimization-based proportional-integral controller during severe grid disturbances. The convergence speed of the proposed adaptive filtering algorithm is compared to that of existing algorithms such as least mean fourth, least mean square, and continue mixed p-norm algorithms. In addition, a comparison is also made with different common optimization methods. The field-programmable gate array-based real-time experimental results are presented to validate the proposed adaptive control topology.