Intelligent Fuzzy Controller Research Articles

A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems

This research study presents the application of the FC-PCC (Fuzzy Logic Predictive Current Control) algorithm in the context of maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system employing a three-level boost converter (TLBC). The proposed approach involves the integration of an intelligent fuzzy controller with a predictive current control strategy in order to improve the performance of MPP tracking. Initially, the utilization of fuzzy logic involves the utilization of data values obtained from the PEMFC. The maximum point (P-I) of the PEMFC polarization curve is determined, followed by the selection of the reference current. A predictive current control technique employs the reference current to ensure the voltage balance of the output capacitor in the three-level converter. The hardware-in-the-loop system utilizes a real-time and high-speed simulator, specifically the PLECS RT Box 1, to obtain the findings. The computational cost of the overall system is rather low, making it feasible to construct using PLECS RT Box 1. The new MPPT algorithm quickly finds the maximum power point (MPP) and balances the voltage of capacitors in a number of different proton exchange membrane fuel cells. The suggested MPPT technique has been verified to demonstrate rapid tracking of the maximum power point (MPP) location, as well as precise balancing of capacitor voltage and robustness to environmental variations. This approach was tested and found to outperform conventional MPPT methods like Perturb and Observe (P&O) and Incremental Conductance (IC) in terms of tracking duration, precision, and voltage balancing, achieving a 15% reduction in tracking duration, a 5% deviation from the MPP value for voltage, and superior stability under changing temperature and pressure.

OPTIMIZATION OF AN INTELLIGENT CONTROLLED BRIDGELESS POSITIVE LUO CONVERTER FOR LOW-CAPACITY ELECTRIC VEHICLES

This paper presents a novel approach to power conversion in electric vehicle (EV) applications. The proposed converter, a Bridgeless Single Stage Positive Luo Converter (BSPLC), is optimized to enhance efficiency and reduce losses in low-capacity EVs. Traditional converters experience higher losses due to their passive components and bridge circuits. By eliminating the bridge components, the converter achieves higher efficiency. An intelligent fuzzy logic controller is employed to provide adaptive control and stabilize output under varying input conditions, improving performance and response time. The converter design is further optimized through parameter tuning and simulation to achieve minimal ripple and maximum power efficiency at 92%. The proposed solution is ideal for low-capacity EVs, as it ensures enhanced power conversion, reduced thermal stress, and improved battery life. The study demonstrates the converter’s capability to meet the growing demands for energy-efficient solutions in modern electric mobility.

Intelligent Fuzzy Traffic Signal Control System for Complex Intersections Using Fuzzy Rule Base Reduction

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs.

Detection of Sybil & DDoS attacks in VANET using intelligent technique

ABSTRACT This paper proposes a novel fuzzy logic controller (FLC) based model referred to as FBM to detect both the DDoS and the Sybil attacks in VANET using an intelligent fuzzy logic controller. A VANET environment is created with different numbers of attackers to check the efficacy of the proposed model using veins, which is used for simulation with an event-based network simulator OMNeT++ and a road traffic simulator SUMO. Furthermore, the performance of the proposed model is compared with a few other machine learning techniques, such as logistic regression (LR), the decision tree (DT), random forest classifier (RF), and the support vector machine (SVM). The proposed FBM model yields better performance than the existing machine-learning techniques. The proposed FBM performs better than RF in accuracy by 0.10492%, precision by 0.08065%, recall by 0.02505%, and F1 score by 0.088243% for the DDoS scenario. It is also observed that the FBM performs better than DT in accuracy by 3.91486%, precision by 14.39994%, and F1 score by 3.819618% for the Sybil scenario. The margin of error for the proposed FBM is also estimated for 95% of the confidence interval and is found to be 0.021691 for the DDoS scenario.

Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press

With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become the trend in the industry for future development. The independent research and development of servo press control systems has become a popular topic and challenge in the domestic press industry. This paper proposes a new type of press transmission mechanism suitable for high-precision stamping, analyzes the working principle of its transmission mechanism, and provides a detailed analysis and discussion on the main research methods of fuzzy intelligent control for servo presses, including fuzzy model and basic control algorithms, fuzzy modeling and control of nonlinear systems, fuzzy predictive control, and stability analysis of fuzzy control systems. Based on the principle of the control scheme, a closed-loop control system block diagram and mathematical model for servo high-precision presses are established. Taking the physical prototype parameters of the STSZG1-250 as an example, the control system is dynamically simulated and verified using Simulink modules. Finally, their role and effects in intelligent control, and prospects for the development of fuzzy intelligent control, are discussed.

Research on the Control Strategy of the Power Shift System of a Cotton Picker Based on a Fuzzy Algorithm

The control strategy of a power shift system has a significant impact on the driving stability and comfort of cotton pickers. To prevent the cotton picker from stopping to shift and reduce the jerks while shifting, in this study, the power shift system of a four-speed cotton picker was taken as the research object, and the working principles of the power shift transmission of the cotton picker and the hydrostatic transmission were analyzed. Firstly, the intelligent fuzzy control strategy of the variable pump and dual variable motor displacement of the cotton picker power shift system was designed using a fuzzy algorithm, and two-input and three-output fuzzy controllers were constructed with the engine rotational speed and vehicle speed as the input parameters, and the variable pump displacement, front motor displacement, and rear motor displacement as the outputs. Secondly, the model of the whole vehicle travel drive system of the cotton picker was constructed using co-simulation of Amesim and Simulink. Finally, the influence of the fuzzy shift control strategy and conventional manual shift method on the speed and driving distance of the cotton picker was compared and analyzed. The analysis results show that the fuzzy algorithm-based control strategy of the power shift system of the cotton picker can ensure that the cotton picker travels stably according to the target speed, and effectively reduces the speed fluctuation and jerks while shifting. The results of the study are of great significance to realize the non-stop shifting of the cotton picker as well as improve the stability and smoothness of the shift while driving.

Fuzzy Logic Controllers Design for the Path Tracking of an Autonomous Coaxial Octorotor

Due to the essential characteristics and utility of coaxial octocopter vehicles in various real-time applications and many actual domains, this article aims to propose intelligent fuzzy logic controllers (FLC) for a six-degrees-of-freedom coaxial octorotor with a dynamic model. Using expert knowledge, the developed control structure is applied to track reference paths in the 3D environment with sufficient stability and effectiveness. The tested octorotor dynamic model is divided into six subsystems: altitude, pitch, roll, yaw, x position, and y position. Meanwhile, a Mamdani fuzzy inference controller is designed and applied for each subsystem to generate the appropriate control action. The simulation and the evolution tasks are done using a MATLAB dynamic model of the controlled process in order to perform and evaluate the proposed intelligent control structures of the 6 DOF octorotor model. The illustrated simulation results demonstrate the designed fuzzy controllers' effectiveness in motion stability and error tracking for different types of paths and all tested variables.

A new power management strategy for PV-FC-based autonomous DC microgrid

Solar energy is widely available in nature and electricity can be easily extracted using solar PV cells. A fuel cell being reliable and environment friendly becomes a good choice for the backup so as to compensate for continuously varying solar irradiation. This paper presents simple control schemes for power management of the DC microgrid consisting of PV modules and fuel cell as energy sources and a hydrogen electrolyzer system for storing the excess power generated. The supercapacitor bank is used as a short term energy storage device for providing the energy buffer whenever sudden fluctuations occur in the input power and the load demand. A new power control strategy is developed for a hydrogen storage system. The performance of the system is assessed with and without the supercapacitor bank and the results are compared. A comparative study of the voltage regulation of the microgrid is presented with the controller of the supercapacitor bank, realized using a traditional PI controller and an intelligent fuzzy logic controller.

FUZZY FRACTIONAL ORDER SLIDING MODE CONTROL FOR OPTIMAL ENERGY CONSUMPTION-TRANSIENT RESPONSE TRADE-OFF IN ROBOTIC SYSTEMS

This work presents a new fuzzy logic parameter tuning-based fractional order sliding mode control (F-FOSMC) strategy for the trajectory tracking of robotic systems. The primary objective is to achieve an optimal control compromise seeking to minimize energy consumption for required motions, all while increasing both accuracy and rapidity of the transient response, addressing therefore a critical challenge in the field of robotics. Our approach integrates a sophisticated fuzzy logic fractional order mechanism to dynamically fine-tune the noninteger derivation parameter of the FOSMC, offering adaptability to varying operational conditions. This intelligent fuzzy logic controller tuning optimizes the FOSMC performance, demonstrates a remarkable capacity to handle uncertainties and disturbances intrinsic to robotic applications and allows to optimize the trade-off between the energy consumption and transient response efficiency throughout the robot’s motion. The effectiveness of the proposed study is thoroughly examined via simulations conducted on a 3 DOF manipulator robotic system. Results showcase that F-FOSMC significantly reduces control energy consumption while preserving rapid and efficient transient response characteristics.

Research on the Application of Intelligent Diagnosis of Major Equipment Failures in Coal Mines

Abstract Currently, the implementation of a coal mine production safety monitoring system, capable of real-time monitoring of the safety condition of large coal mine equipment and timely dispatch management, is crucial for the efficient and safe operation of coal mine enterprises. This paper designs the coal mine major equipment failure process based on the rolling bearing’s vibration mechanism, analyzes the major equipment’s failure signal using both time domain and frequency domain analysis methods, and employs the intelligent fuzzy control method to identify the major equipment failure. We have designed and practically applied a real-time, multi-threaded, and comprehensive fault diagnosis and prediction system for coal mine major equipment, combining information from coal mine safety, production, and operation. The results show that the normal state’s time-domain amplitude approximates (-0.2, 0.2), and the time-domain waveforms all display periodic shock signals when the bearing sustains damage. The faulty vibration amplitude of the inner ring is within 0.08 ms 2 and the faulty vibration amplitude of the outer ring is within 0.44 ms 2, and the diagnostic accuracy using the method of this paper reaches 97.2%. This study demonstrates the effectiveness of remote monitoring and fault analysis of major equipment in coal mines, which effectively improves the equipment start-up rate and ensures coal mine production safety.

Soft Computing Algorithm-Based Intelligent Fuzzy Controller for Enhancing the Network Stability of IPS

The work furnished in this paper projects the application of fuzzy-aided tilt-integral-derivative (TID) with filter (TIDF) for regulating the dynamical behavior of the electrical interconnected power system (IPS) during uncertain conditions. The optimal design of the fuzzy TIDF is performed using the efficient soft computing technique of the water cycle algorithm (WCA). The suggested fuzzy TIDF controller performance is tested on the multi-area diverse source realistic (MADSR) IPS (MADSRIPS) subjected to control area 1 with step load disturbance (SLD) of 10%. Moreover, the analysis carried out on MADSRIPS is considered with the communication time delays (CTDs) for the investigation near to realistic environment. The efficacy of fuzzy-aided TIDF is contrasted with reported controllers available in literature, and the simulation analysis is acquired from the framework of MATLAB, which revealed the sovereignty of the suggested controller. Later, the MADSRIPS is operated with the integration of a HVDC line in conjunction with the tie-line to improve the performance.

Unit vector template control strategy‐based harmonic mitigation and charging with three phase–three level–three switch Vienna rectifier for Level 3 electric vehicle charging applications

SummaryFast battery charging units act as non‐linear loads, which induce a more harmonic effect in the utility grid. Hence, maintaining the stable voltage, current ripple, and total harmonic distortions within the permissible IEEE std level during the charging time of grid to electric vehicle (G2V) has become an important task for researchers in the future. In order to mitigate power fluctuations during every charging cycle, it will enhance the battery life cycle in electric vehicles (EVs). Three phase–three level–three switch Vienna rectifier with fuzzy logic controller tuned PID controller is used here for AC/DC conversion with minimum switching loss. A fuzzy intelligent control strategy has been employed with the DC link voltage regulator in order to sustain the DC link voltage as constant and progress the voltage/current profile by injecting active power at the point of common coupling. Unit vector template (UVT)‐based control strategy is employed here to identify the fundamental components from the load side and also estimate the reference phase current. This scheme enhances the performance and reduces the mathematical system complexity to diminish the harmonics in the distribution grid. The adaptability and feasibility of this proposed control method have been verified by the MATLAB simulation tool. To validate the response of the Vienna Rectifier with UVT controller are simulated with different unbalanced load conditions and also verified with 30 kW laboratory prototype experimental results using dsPIC30F4011 controller and the switch is IRFP260 with power diode RHRG30120.

Application of Intelligent Control in Chromatography Separation Process

Chromatographic separation plays a pivotal role in the manufacturing of chemical products and biopharmaceuticals. This technique exploits differences in distribution between stationary and mobile phases to separate mixtures, impacting final product quality. Simulated moving bed (SMB) technology, recognized for its continuous feed, enhances efficiency and increases production capacity while reducing solvent and water consumption. Despite its complexity in controlling variables like flow rates and valve switching times, traditional control theories fall short. This study introduces an intelligent fuzzy controller resembling an approximate neural network (NN) for SMB control. Simulation results demonstrate the controller’s effectiveness in achieving desirable outcomes for the SMB system.

A Centralized Stage-Wise Control Approach for Frequency and Voltage Regulation in PV–Wind–Storage Based Islanded Microgrid

In a microgrid, perturbations stemming from uncertain renewable sources and sudden load fluctuations can result in deviations in system voltage, frequency, and more, leading to instability. Thus, focusing on achieving stable microgrid operation, this paper proposes a novel stage-wise control approach for regulating the frequency and voltage of islanded microgrids. The approach employs an intelligent fuzzy logic controller with 64 rules. Specifically, the control approach is divided into three stages. In stage 1, the proposed approach manages the system under minor perturbations to regulate all system parameters within a feasible operating range, utilizing a battery as ancillary support. Furthermore, during significant perturbations, stages 2 and 3 of the proposed approach involve percentage-wise shedding non-critical loads to enhance stable microgrid operation. The load-shedding process continues until the system parameters stabilize within an acceptable operating range. The performance of the proposed approach is validated using MATLAB software and is compared against existing topologies. Notably, it has been observed that in the presence of significant small and large perturbations, the system demonstrates considerably lower deviations, following the recommended standards, i.e., 0.57 and 0.72% voltage deviation, including 0.14 and 0.17% frequency deviation. This observation emphasizes the superiority of the proposed control approach.

Research on vibration reduction of long span cable stayed bridge under fuzzy control of two kinds of membership function

In order to control the collision of long-span cable-stayed bridges with floating systems caused by large longitudinal displacement of beam ends under earthquake action, based on the engineering background of a long-span dual-purpose double-tower cable-stayed bridge, the collision problem of the main approach bridge under earthquake action is proposed. Two kinds of intelligent fuzzy control strategies based on different membership functions are presented, using MR Damper as a control device and modeled by SIMULINK. In this paper, the damping control effect of the proposed fuzzy control strategy on the collision problem of the main approach bridge of the cable-stayed bridge is studied. The results show that changing the range of the membership function will change the size of the control current, and the longitudinal displacement and acceleration of the structure will decrease with the increase in the control current. However, when the control current is too large, the acceleration response will be amplified and oscillated. Both fuzzy controls can reduce collision times, and the peak value of collision force between the main approach bridges and the energy consumption of the MR damper are roughly similar. Based on the control effect of longitudinal displacement response, acceleration response, and collision force response of the main approach bridge and the energy consumption of the damper, fuzzy control A is better than fuzzy control B.

Optimal intelligent fuzzy TID controller for an uncertain level process with actuator and system faults: Population-based metaheuristic approach

For the Conical Tow-Tank Non-Interacting Level (CTTNL) prototype system, this article discusses the design of an unique optimal fractional order TID controller employing a type-1 fuzzy set. To generate a linearized model transfer function for the nonlinear CTTNL process, local linearization around the equilibrium state was performed. This work compares the performance of a fuzzy tilted-integral-derivative (FTID) controller for the CTTNL system to a traditional proportional–integral–derivative (PID) controller. The parameters of the FTID controller were optimized using the Chicken Search Optimization (CSO) algorithm in this study, and those values were used to create a PID controller. Thereafter, the FTID and PID controllers’ performance is compared. The investigation shows that the FTID controller beats the PID controller in terms of IAE, ISE, ITAE, and ITSE integral errors. The suggested controller’s fault-tolerant performance was also assessed using the fault recovery time (Frt) parameter. The FTID controller outperforms traditional PID controllers in terms of transient and steady-state performance. The fault tolerance capabilities of the FTID controller will be tested on the CTTNL system in the future, using additive and multiplicative failures. Further research will include a comprehensive examination of robustness in the context of model parameter uncertainties.

Benefits of Intelligent Fuzzy Controllers in Comparison to Classical Methods for Adaptive Optics

Adaptive Optics (AO) systems have been developed throughout recent decades as a strategy to compensate for the effects of atmospheric turbulence, primarily caused by poor astronomical seeing. These systems reduce the wavefront distortions using deformable mirrors. Several AO simulation tools have been developed, such as the Object-Oriented, MATLAB, and Adaptive Optics Toolbox (OOMAO), to assist in the project of AO. However, the main AO simulators focus on AO models, not prioritizing the different control techniques. Moreover, the commonly applied control strategies in ground-based telescopes are based on Integral (I) or Proportional-Integral (PI) controllers. This work proposes the integration of OOMAO models to Simulink to support the development of advanced controllers and compares traditional controllers with intelligent systems based on fuzzy logic. The controllers were compared in three scenarios of different turbulence and atmosphere conditions. The simulations were performed using the characteristics/parameters of the Southern Astrophysical Research (SOAR) telescope and assessed with the Full Width at Half Maximum (FWHM), Half Light Radius (HLR), and Strehl ratio metrics to compare the performance of the controllers. The results demonstrate that adaptive optics can be satisfactorily simulated in OOMAO adapted to Simulink and thus further increase the number of control strategies available to OOMAO. The comparative results between the MATLAB script and the Simulink blocks designed showed a maximum relative error of 3% in the Strehl ratio and 1.59% in the FWHM measurement. In the assessment of the control algorithms, the fuzzy PI controller reported a 25% increase in the FWHM metrics in the critical scenario when compared with open-loop metrics. Furthermore, the fuzzy PI controller outperformed the results when compared with the I and PI controllers. The findings underscore the constraints of conventional control methods, whereas the implementation of fuzzy-based controllers showcases the promise of intelligent approaches in enhancing control performance under challenging atmospheric conditions.

Optimal design, intelligent fuzzy logic and model predictive control for high-purity ethyl-methyl carbonate and diethyl carbonate production using reactive dividing wall column

Methyl carbonate (EMC) and Diethyl carbonate (DEC), as excellent solvents for lithium-ion electrolytes, are synthesized by transesterification, and consecutive reaction seriously complicate optimization and control issues. Process synthesis, parameters optimization and advanced control are crucial points that should be addressed for this ill-conditioned and highly nonlinear system, which is the motivation and novelty of present work. With single-objective and multi-objective genetic algorithm, intensified processes for EMC and DEC production were optimized respectively with heterogeneous catalyst. And reactive dividing wall column achieved 23% and 13% total annualized cost reductions compared with reactive distillation for two target carbonates. Based on optimal steady-state solution, intelligent fuzzy logic and model predictive control of EMC and DEC production is conducted to clarify their respective strengths in intensified process with consecutive reaction. Sequential optimization for parameters of FLC was carried out with Integral of Squared Error (ISE) as objective. Finally, control performance of FLC and LPMC are compared with that under PID control, and results show that overshoot and transition time under FLC and LMPC are obviously mitigated, along with evident reduction in ISE.

Research on improved MPPT Fuzzy Logic Control-Incremental Conductance Algorithm

This research discusses the limitations of the Maximum Power Point Tracking (MPPT) incremental behaviour algorithm. Although MPPT's incremental behaviour algorithm is simple and easy to implement, despite its usefulness, this method is beset by several limitations which include a slow convergence rate towards the optimal operating point, significant oscillations surrounding the maximum power point at steady state, and momentary system movement away from the maximum power point after sudden changes or variations in irradiation. For these reasons, an improved MPPT Fuzzy Logic Control-Incremental conductance (FLC-IC) algorithm is proposed in this study. And the adjustment in the input variables of the MPPT Incremental Conductance algorithm controlled by the fuzzy intelligent control algorithm increases the convergence speed, decreases the oscillations, and remains stable despite radiation variations. The algorithm is simulated and applied in a charge controller that operates using the solar energy, and the outputs observed highlights the effectiveness of the proposed algorithm that is proposed over the IC algorithm in terms of speed and efficiency.

Comparison of Intelligent Fuzzy Controller and Fuzzy Rule Suram Algorithms in the Drying Process

Comparison of Intelligent Fuzzy Controller and Fuzzy Rule Suram Algorithms in the Drying Process