Fuzzy Logic Control Approach Research Articles

A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system

Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that use accessible, non-idealized predictive information while improving their efficiency for successive rainfall events instead of specific phases. This study developed a predictive fuzzy logic and rule-based control (PFL-RBC) approach to address the continuous control of individual storage systems. This approach incorporates total rainfall depth forecast information with an intra-storm fuzzy logic system to optimize peak flow control and several rule-based strategies for pre-storm water detention, reuse, and release control. Computational experiments were conducted using a storage tank case study to test the proposed approach under various rainfall conditions and storage sizes. The results showed that PFL-RBC outperformed static rule-based control in infrequent design storms and realistic continuous rainfall events, reducing flood peaks and volumes by 55 %∼87 % and 7 %∼20 %, respectively, and significantly increasing water detention time and reuse volume. Meanwhile, PFL-RBC required less predictive information to achieve a 6 %∼15 % advantage in peak flow control compared to optimized model predictive control. More importantly, PFL-RBC was reliable in the face of input uncertainty, with <25 % performance loss for water quantity control when the realistic forecast error ranged from -50 % to +50 %. These findings suggest that the proposed approach has great potential to enhance the efficiency and practicality of stormwater storage operations.

Evaluation of advanced control strategies for PMSG in wind turbines: backstepping fuzzy logic control and adaptive neural network control approaches

This study presents a comparative analysis of two advanced control strategies for Permanent Magnet Synchronous Generators (PMSGs) in wind turbines: Backstepping Fuzzy Logic Control and Adaptive Neural Network Control. The aim is to evaluate their effectiveness in handling structured and unstructured uncertainties and enhancing system performance. Backstepping Fuzzy Logic Control integrates backstepping techniques with fuzzy logic regulation to improve flexibility and robustness. This method addresses the limitations of traditional PI controllers by enhancing adaptability to parameter variations and external disturbances. In contrast, Adaptive Neural Network Control employs neural networks with dynamic compensators to address high uncertainties and nonlinearities, aiming to surpass the performance of conventional PI controllers. The study involves simulations to compare these control strategies based on several criteria: robustness in managing parameter changes and external disturbances, flexibility in adapting to dynamic conditions, and overall performance including efficiency and stability. Simulation results indicate that both strategies offer significant improvements over traditional PI controllers. Backstepping Fuzzy Logic Control demonstrates strong adaptability and robustness, while Adaptive Neural Network Control shows superior efficiency and optimality, particularly in high-uncertainty environments. This comparative analysis provides insights into the strengths and limitations of each approach, offering guidance for selecting the most suitable control strategy based on specific operational requirements. The findings contribute to advancing control methodologies in wind energy systems and suggest directions for future research and practical implementation.

Design of a novel intelligent cooperative type-2 fuzzy logic controller and fractional-order synergetic approach for wind energy systems based MPPT methodology

Design of a novel intelligent cooperative type-2 fuzzy logic controller and fractional-order synergetic approach for wind energy systems based MPPT methodology

Design of PV, Battery, and Supercapacitor-Based Bidirectional DC-DC Converter Using Fuzzy Logic Controller for HESS in DC Microgrid

Renewable energy sources (RES) are becoming more popular globally as a reaction to critical energy concerns. Modern energy management technologies are used to maximize their efficiency while preserving the reliability of the grid. A hybrid energy storage system (HESS) connects to the DC microgrid through the bidirectional converter, allowing energy to be transferred among the battery and supercapacitor (SC). In this paper, a fuzzy logic control (FLC) technique is developed for PV-based DC microgrid systems that use both batteries and SCs. The proposed method uses the unbalanced energy from the battery pack to enhance the overall effectiveness of the HESS. The FLC approach is performed to validate under conditions of variable irradiance using MATLAB Simulink. When sudden changes in irradiance occur, the proposed FLC brings the voltage back to the desired level in terms of transient response like 33 ms settling times and 19% overshoot values. The results exhibit that the proposed method is more efficient in terms of time response, power output, increasing battery life, and ensuring a continuous supply of the PV system.

Bağımsız Bir Hibrit Güç Sisteminin Tasarımı ve Akıllı MPPT Algoritmaları ile Optimizasyon Kontrolü

In this paper, a stand-alone hybrid power system has been proposed using both: photovoltaic generator (PVG) and permanent magnets synchronous generator (PMSG), integrated with a lithium-type battery connected to the DC bus. The improvement of performance and reliability of the overall system are guaranteed. For this aim, the efficiency of the PVG system can be enhanced firstly by using two MPPT's techniques, such as the Perturb and Observe (P&amp;amp;O) method and the Fuzzy Logic Control (FLC) approach. The Genetic Algorithms (GA’s), using the principles of evolution, natural selection, and genetic mutation, are then introduced to address difficulties in the adjustment of the FLC gains. A lithium-ion battery model is presented to ensure stability and energy storage. Furthermore, the model of the wind energy conversion system (WECS) based on PMSG is presented. Hence, the control by the gradient method, allowing designing an MPPT based on the delivered power and mechanical speed of the generator. Both sources are connected to the grid via a two-level voltage source inverter controlled by Space Vector Modulation (SVM) technique. Finally, simulation results obtained using MATLAB / SIMULINK software proved the effectiveness of proposed control strategies with high performance which is manifested clearly when the suggested controllers employed and shed light on the performance of the stand-alone hybrid system.

Design and stabilization of a Coandă effect-based UAV: Comparative study between fuzzy logic and PID control approaches

Recent years have experienced a notable surge in unmanned aerial vehicles (UAV) research, prompting exploration into innovative concepts. This paper introduces a compact UAV harnessing the Coandă effect, an underexplored phenomenon in fluid mechanics. Featuring a single lift motor and two types of flaps, this UAV offers exceptional maneuverability, presenting significant challenges compared to conventional multi-rotor UAVs. To address these challenges, we explore the theoretical study, mechatronic design, and manufacturing complexities of the Coandă UAV. Emphasizing the distinctiveness of our work, we assess a Fuzzy Logic Controller (FLC) for UAV stabilization, marking the first application of such techniques to a Coandă-effect UAV, in contrast to the Proportional-Integral-Derivative (PID) control employed by other researchers. This innovative application of Fuzzy logic, particularly the Sugeno model, proves advantageous, offering faster and more robust control in uncertain or noisy environments. The proposed FLC strategy is systematically compared against a classical PID control approach, formulated based on the Mamdani and Sugeno models, optimized and manually tuned using a genetic algorithm. Our results showcase a significantly improved settling time of 0.417 s with the FLC strategy, surpassing the PID control approach by 35.23%. To substantiate our findings, we present comprehensive experimentation conducted at both software and hardware levels using Matlab® and Simulink for a microcontroller-based UAV. This groundbreaking fusion of novel design and advanced control techniques not only addresses the unique challenges posed by the Coandă UAV's aerodynamics but also contributes significantly to the field of UAV research.

Concert Exploration of DFIG Grid-integrated MMC using Artificial Intelligence

The performance of grid-connected wind turbine systems that use a Doubly Fed Induction Generator (DFIG) is explored in this work. In the suggested configuration, two conversion stages are present. In the first step, Alternating Current (AC) voltage is changed into Direct Current (DC) voltage. At this point, the conventional two-level converter is implemented. The second stage involves the conversion of DC to AC using a five-level Modular Multilevel Converter (MMC). Voltage fluctuation is observed in the DC link. It is anticipated that the Fuzzy Logic Control (FLC) approach will be able to adjust the DC link voltage at the MMC of the second stage of conversion. For the purpose of the Pulse Width Modulation (PWM) system, the current controller is accountable for regulating the current signals and the reference signals. As a result, the PI Controller (PIC) is being evaluated for use in this function. Carrier-based Phase Disposition (PD) PWM is employed for MMC switch control. The proposed controllers’ performance is validated in Matlab/Simulink.

High-Performance Induction Motor Speed Control Using a Robust Hybrid Controller With a Supertwisting Sliding Mode Load Disturbance Observer

To enhance the speed control performance of a three-phase induction motor (IM) controlled by the vector control strategy, a new design of a hybrid controller (HC) is proposed based on the super-twisting algorithm (STA) and fuzzy approach. STA is chosen for its ability to decrease the ingrained chattering phenomenon in the classical sliding mode control (SMC) with maintaining tracking precision and robustness. Nevertheless, the control gains included in the control law have an evident impact on suppressing the chattering phenomenon and increasing the system's dynamic response speed. Therefore, firstly, a robust HC based on the fuzzy logic control (FLC) approach that operates as a fuzzy supervisor to on-line self-tune the value of the gains according to the system states is suggested to achieve high dynamic performance and limit the chattering effect. Secondly, to enhance the disturbance refusal capability, a super-twisting sliding mode load disturbance observer (STSMLDO) is developed to estimate the load torque disturbances. Then the estimated disturbance is introduced into the equivalent control law. Subsequently, the system stability is verified by the Lyapunov theorem. Finally, the superiority of the proposed scheme is validated through comparison with the advanced and traditional controllers in simulation and experimental studies.

Enhanced Distributed Non-Linear Voltage Regulation and Power Apportion Technique for an Islanded DC Microgrid

There is a growing focus on exploring direct current (DC) microgrids in traditional power grids. A key challenge in operating these microgrids is ensuring proper current distribution among converters. While conventional droop control has been used to address this issue, it requires compensating for voltage deviations in the DC bus. This paper introduces an innovative distributed secondary control approach that effectively addresses both voltage restoration and current sharing challenges within a standalone DC microgrid. The distributed secondary control proposed in this study is integrated into the microgrid’s cyber layer, enabling information sharing between controllers. This distributed approach ensures reliability, even in the event of partial communication connection failures. The controller employs a fuzzy logic control approach to dynamically determine the parameters of the secondary control, resulting in an enhanced control response. Additionally, the proposed approach can handle constant power and resistive loads without specific requirements. Employing the Lyapunov method, we have derived adequate stability conditions for the proposed controller. The performance of the controller has been assessed using MATLAB/Simulink® models and validated with real-time experimental testing performed with a SpeedgoatTM real-time machine, considering five different test cases. The results indicated that the proposed control system is robust in achieving its control objectives within a DC microgrid, exhibiting fast response and minimal oscillations.

A Soft Computing Intelligent Control Algorithm to Extract Maximum Energy from Solar Panel

Utilizing soft computing, a maximum power point tracking (maximum PPT) control algorithm is developed, and its performance is compared to that of more traditional Lead Acid battery charging methods such as incremental conductance technique-based maximum PPT. Since the power vs voltage graph of a photovoltaic (PV) cell is nonlinear, a suitable control method seeks to obtain the highest power under dynamic conditions. In order to construct a PV cell with the maximum PPT, a fuzzy logic control approach known as soft computing is used. The cell active energy is used to charge the lead acid battery. A fuzzy logic compares its performance with the incremental conductance technique under dynamic conditions. Moreover, dc to dc converter is required to maintain constant output voltage to charge the battery under low level voltage. A zeta converter is taken to maintain output voltage under various insolation. The significance of algorithm is demonstrated by MATLAB Simulation results and hardware results.

Optimal Real-time implementation of fuzzy logic control strategy for performance enhancement of autonomous microgrids

Distributed power grid systems require more intelligent and adaptable control management and optimization to preserve the best performance. These balance the generated power source and load demand even during severe interruptions. This research proposes a comparative analysis of an autonomous microgrid system optimized by fuzzy logic control and proportional-integral controller. The microgrid fuzzy logic control parameters are designed using the African vulture and Particle swarm optimization algorithms. At the same time, the proportional-integral controller is designed by using the Gorilla troops optimization algorithm. The suggested microgrid control techniques are verified using a MATLAB/Simulink environment. The overall system's detailed modeling and control methodologies are also presented. The investigation of the fuzzy logic control approach is also tested in a real-time setting. It is implemented by using OPAL real-time 4510 rapid control prototyping. The attained results prove the superiority of the fuzzy logic controller based on the African vulture optimization technique compared with other optimal control techniques. It is applied to give an ideal design for the fuzzy logic control parameters to preserve adequate power to the different loads. The proposed optimal controller is able to bring a robust and effective control with an accuracy of approximately 4%, 5%, 28%, and 26% than other techniques under different scenarios.Index Terms: African Vultures Optimization algorithm (AVOA), Autonomous Microgrid, Decentralized energy generation, Fuzzy Logic Control, Opal RT-LAB, Proportional plus integral controller.

Sliding-mode observer–based active fault-tolerance control for a two-degree-of-freedom helicopter system with sensor faults

In this paper, an active fault-tolerance controller is proposed for a two-degree-of-freedom (2-DOF) helicopter model with different types of sensor faults. The proposed controller is designed by combining an interval type-2 fuzzy logic control approach with a sliding-mode control technique. In addition, the controller is compensated by a sliding-mode observer used to estimate faults in real-time. For the proposed control scheme, the overall stability of the closed-loop system is proven using the Lyapunov stability theory. Finally, numerical simulations and experiments verify that the proposed scheme achieves superior control performance in the case of sensor failure.

Fuzzy Logic Controller for Temperature Control

This paper describes the use of MATLAB in the design and implementation of a temperature control system for agricultural applications based on fuzzy logic. The system uses two inputs—the user-specified target temperature and the temperature sensed from the agricultural field—to produce two outputs for positive and negative pulse width modulation (P-PWM-C and N-PWM-C). With membership functions defined for the input and output ranges, the fuzzy logic controller is designed using the MATLAB Fuzzy Logic Designer. Ten guidelines are created to regulate the temperature control procedure. The relationship between the inputs and outputs is shown in the 3D surface viewer, which sheds light on the fuzzy logic control approach.

Vehicle anti-lock brake system - dynamic modeling and simulation based on MATLAB Simulink and CarSim

The anti-lock brake system (ABS) is a safety feature for automobiles that regulates brake pressure and delivers predictable braking in the majority of circumstances by monitoring wheel lock-up. It necessitates the creation of a robust control system due to the unknown uncertainties on the braking system induced by altering the vehicle model dynamics and road conditions. To progressively decrease the automobile accidents caused by abrupt braking and will achieve the best brake performance. The mathematical model was developed considering vehicle speed, and slip ratio between the tire and the road conditions and simulation was done for Super Urban Vehicle of the ABS using Fuzzy logic controller and CarSim software in MATLAB/Simulink are the main objectives of this study. With the assistance of the fuzzy logic control approach and the MATLAB/Simulink software, a quarter-vehicle dynamic model was developed to simulate under a variety of road circumstances, such as wet and dry conditions, and at different speeds. Finally, it was determined that the braking distance was decreased and the vehicle stability was maintained during the simulation process by comparing the results of the simulation performed using the CarSim software connected to MATLAB/Simulink and the performance of the built controller.

Stick-slip stabilization in oil well drill-strings via optimal hybrid fractional order fuzzy logic control scheme

Stick-slip vibration is regarded as one of the major factors that negatively influence the efficiency of the drilling processes. Therefore, it would be highly preferable to overcome the stick-slip vibration challenge through an effective control scheme. This study aims to address the stick-slip vibration problem in oil well drill-strings through a novel concept of an optimal hybrid fractional order fuzzy logic control (OH-FOFLC) scheme. By combining the robustness of fuzzy logic with the convergence speed of the fractional order method, the proposed scheme may enhance tracking performance in drill-strings under various operating conditions. In addition, the fuzzy logic controller for the OH-FOFLC scheme was designed by considering the minimum rules to decrease the computational burden as well as to reduce the controller’s cost. Furthermore, a particle swarm optimization algorithm was implemented for optimizing the coefficients of the OH-FOFLC scheme. To illustrate the overall performance and effectiveness of the OH-FOFLC, four different controllers, including proportional–integral–derivative (PID), fractional order PID, sliding mode control, and optimal hybrid fuzzy logic control approaches, are considered. The validity of the suggested OH-FOFLC was demonstrated through numerical simulations and comprehensive comparative studies which demonstrated that the OH-FOFLC scheme performs better than the four other controllers under various operating conditions.

Chaotic-Billiards Optimization Algorithm-Based Optimal FLC Approach for Stability Enhancement of Grid-Tied Wind Power Plants

Globally,wind power plants (WPPs) installations are dramatically increasing, leading to a large-scale integration into the power grids. Huge endeavors are devoted to achieving an efficient operation of WPPs. This paper offers a novel chaotic-billiards optimizer (C-BO) algorithm to properly design the fuzzy logic control (FLC) strategy for stability enhancement of grid-integrated WPPs. The C-BO algorithm has distinct features such as simple construction, low numbers of parameters required to design, high convergence speed, and low computational burden. The permanent-magnet synchronous generator (PMSG)-based WPP is interlinked into the electric power grid through power converters. A cascaded FLC approach, which is optimally fine-tuned by the C-BO, is offered as the control methodology for these converters. The C-BO algorithm fine-tunes the gain factors of multiple FLCs at a rapid convergence speed. A simulation-based optimization approach is applied, in which the integrated square error criterion is chosen as an objective function. For the sake of preciseness, the PMSG-based WPP is connected to the IEEE 39-bus New England test system. The effectiveness of the optimal FLC using the C-BO algorithm is compared with that achieved using the water cycle algorithm-based optimal FLC, the genetic algorithm-based optimal FLC, and the marine predator algorithm-based optimal proportional-integral controller, taking into account severe grid disturbances. Moreover, real wind speed measured data captured from Zaafarana Station in Egypt are extensively performed in the system studies. The validity of the offered control approach is widely verified by the simulation analyses using the MATLAB/Simulink environment.

Developing hierarchical fuzzy logic controllers to improve the energy efficiency and cutting rate stabilization of natural stone block-cutting machines

In the natural stone sector, rock blocks are cut as a plate using block-cutting machines (BCM), and the plates are polished in the polishing machines. Energy consumption is the primary operating cost in both the cutting and polishing processes. The energy consumption of BCMs is affected by many factors, with the most influential being traverse speed. The energy efficiency of BCMs can be improved by regulating the traverse speed throughout the cutting process, and the roughness of the cut surface can be reduced by keeping the ratio between the traverse speed and peripheral speed constant as an additional benefit. This also reduces energy consumption and waste production in the polishing process. In this paper, two controllers used for the regulation of traverse and peripheral speeds are applied to a laboratory-scale BCM, one of which regulates only the traverse speed, while the other regulates both the traverse and peripheral speeds. A limped hierarchical fuzzy logic controller (LHFLC) approach is used in the design of the controllers for the first time on the BCM. In the proposed LHFLC method, the design procedure of the controller is simplified, and its memory usage, computational load, and response time are decreased. The performance of the controllers (a three-input/one-output (3I1O) FLC, 3I1O LHFLC, and a three-input/two-output (3I2O) LHFLC) was tested with cutting experiments performed on three rock samples (Bilecik Beige limestone, Uşak Green marble, and Afyon travertine), and the acquired results reveal that the proposed controllers (3I1O LHFLC and 3I2O LHFLC) are more efficient than the previous controller (3I1O FLC), with over 21%. A statistical analysis shows that to improve the energy efficiency of the BCM, the 3I1O LHFLC could be used rather than the 3I1O FLC. It is further noted that statistically, the cutting rate can be kept more constant with the 3I2O LHFLC than with the 3I1O LHFLC, meaning that the surface roughness of the plates produced by the BCM can be reduced using the 3I2O LHFLC.

Performance Assessment of Fuzzy Logic Control Approach for MR-Damper Based-Transfemoral Prosthetic Leg

Transfemoral amputation commonly occurs due to some stroke, diabetes, physical or mental trauma, which reduces the person's movement capability. Therefore, an efficient prosthetic leg is essential to improve the life of an amputee by replacing the lost limb. This letter addresses the fuzzy logic-based control strategy for magneto-rheological damper based prosthetic leg for transfemoral amputees. The primary focus of this letter is to present the performance analysis of the control to achieve the entire gait cycle (both swing and stance phases) for a transfemoral prosthesis. The performance and robustness analysis of the developed prosthetic leg is tested for real-time gait data to obtain the precise and more natural gait by the transfemoral amputee.

Control points searching algorithm for multiple mobile robots

In the robotic field, the important issue is to find the adequate path for the mobile robot navigation with free collision. This paper describes the development of the navigation strategy for multiple mobile robots. The present work investigates two types of navigation strategies: navigation with one robot and navigation with two robots. The proposed algorithm is based on searching the value of the visibility condition of the trajectory to detect the collision problem. Then, control points are selected to find the safe path for navigation. On the other hand, a sliding mode controller (SMC) is used for tracking the planned trajectory. Hence, to improve the performance given by the SMC, a fuzzy logic control approach is applied for tracking the desired trajectory. Finally, simulation results are given to highlight the applicability of the proposed approach.

Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency