Self-tuning Fuzzy Controller Research Articles

Application of PID and self-tuning fuzzy PID control methods in the control of non-linear magnetic levitation system

Because of the Magnetic Levitation System's (MLS) low energy consumption and little friction two factors that are deemed crucial for unstable and nonlinear systems MLS research is now being conducted in the engineering area. This article discusses the comparison of the performance of control theories used by applying advanced control theories to cope with the complexity of the structure and controllability difficulties of MLS. The control methods compared are Proportional–Integral–Derivative (PID) and Self-Tuning Fuzzy PID (STFPID) methods. These methods were developed in the MATLAB environment. The MLS model created in the MATLAB environment was subjected to the suggested control methods, and the outcomes were compared. The outcomes unequivocally demonstrate that MLS location control may make use of PID and STFPID techniques. Four criteria were used to compare the developed control approaches performances. These are the criteria; rise time, settling time, percent maximum overshoot and overshoot value. It is clearly seen in the results that the STFPID control method provides control of the MLS with greater stability than the PID control method.

Self fuzzy-genetic approach for boosting photovoltaic efficiency

The generation of power in Photovoltaic systems is reduced when they operate far from their maximum power point. For optimal operation, it is essential to continuously track the maximum power point of the PV solar array. However, identifying the maximum power point is a challenge due to the nonlinear relationship of electrical characteristics of PV panels with external factors. To address this issue, we present a novel design approach for a self-organizing, self-tuning fuzzy logic controller, applied to the problem of maximum power point tracking in photovoltaic systems. We outline the basic structure of the fuzzy logic controller and address the design problems typically associated with conventional trial-and-error schemes. We also discuss the suitability of the genetic algorithm optimization technique for determining and optimizing the fuzzy logic controller design. In our proposed approach, we translate the normalization factors, membership function parameters, and controller policy into bit-strings, which are then processed by the genetic algorithm to find a near-optimal solution. To achieve high dynamic performance, we choose a particular objective function as a performance index. We compare our approach with two variants of the maximum power point algorithm, one based on genetic algorithms and the other based on fuzzy logic, as well as with the methods described in references [34] and [35], in order to evaluate its effectiveness.

Enhancing Urban Mobility with Self-Tuning Fuzzy Logic Controllers for Power-Assisted Bicycles in Smart Cities.

In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders.

A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a grid-linked photovoltaic system.

Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS's DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.

Perancangan Self Tuning Dua Derajat Kebebasan dengan Metode Fuzzy Logic pada Pengendalian Kecepatan Motor Sinkron Magnet Permanen Menggunakan Metode Vector Control

Abstract — This paper proposes self tuning Proportional-Integral/Proportional gain controller parameter in a Two Degree of Freedom (2DoF) using fuzzy logic method for speed control of permanent magnet synchronous motor. The self tuning method can automatically determine the PI-P parameters using a fuzzy logic control system. Speed control of the 3-phase permanent magnet synchronous motor is carried out by changing the 3-phase system (a-b-c) to a 2-phase system (direct-quadrature) using the vector control method. SVPWM method is used to modulate six switches on a 3 phase inverter. The results obtained using MATLAB Simulink show that by using a self-tuning fuzzy logic control system, high motor performance results are obtained, where the speed response of the permanent magnet synchronous motor shows that the step response and disturbance response are close to the ideal response.
 Keywords— Permanent Magnet Synchronous Motor, Self Tuning, Two Degrees of Freedom, Fuzzy Logic, Vector Control.

Variable Clustered Fuzzy Rules for Self-Tuning Scheme for Respiratory Distresses by Similarity and the Phase Plane Trajectory Concept

This work proposes a fuzzy rule extraction methodology for a self-tuning fuzzy controller based on the fuzzy clustering method (FCM) and the similarity approach technique. The similarity technique and phase plane trajectory method are used to even lower the acquired rules. To show a potential rule extraction scheme, the self-tuning fuzzy-logic-based proportional-derivative (STFLPDC) with 49 expert fuzzy rules and 49 clustered fuzzy gain rules is used. The utility of the approach is validated using common clustering validity indices. With 49 initial clustered fuzzy rules, the suggested method addressed oxygen supply in a human respiratory model. After further reduction using the similarity strategy, 29 and 14 fuzzy rules remained. Parallel to this, a real-time benchmark application using 49, 21, and 16 extracted fuzzy gain rules is used to access the performance of a lab-based overhead crane. Finally, to investigate control performance in both models, the phase plane trajectory concept is used to generate as few as 13 fuzzy gain rules.

Fuzzy Cooperative Control for the Stabilization of the Rotating Inverted Pendulum System

The rotating inverted pendulum is a nonlinear, multivariate, strongly coupled unstable system, and studying it can effectively reflect many typical control problems. In this paper, a parameter self-tuning fuzzy controller is proposed to perform the balance control of a single rotating inverted pendulum. Particle swarm optimization is used to adjust its control parameters, and simulation experiments are performed to show that the system can achieve stability with the designed parametric self-tuning fuzzy controller, with control performance better than that of the conventional fuzzy controller. Furthermore, the leader-follower control strategy is used to realize the cooperative control of multiple rotating inverted pendulums. Two QUBE-Servo 2 rotating inverted pendulums are used for a cooperative pendulum swing-up experiment and stabilization experiment, and the effectiveness of the proposed cooperative control strategy is verified.

2-DoF Self-tuning Fuzzy PID Controller for Under-actuated Inverted Pendulum System with Uncertainties in Model

2-DoF Self-tuning Fuzzy PID Controller for Under-actuated Inverted Pendulum System with Uncertainties in Model

Adaptive Neuro-Fuzzy Self Tuned-PID Controller for Stabilization of Core Power in a Pressurized Water Reactor

There has been a lot of interest in generating electricity using nuclear energy recently. This interest is due to the features of such a source of energy. The main part of the nuclear energy system is the reactor core, especially the most widely used Pressurized Water Reactor (PWR). This reactor is the hottest part of the nuclear system; security risks and economic possibilities must be considered. Controlling this reactor can increase the security and efficiency of nuclear power systems. This study presents a dynamic model of the (PWR), including the reactor's core, the plenums of the upper and lower, and the connecting piping between the reactor core and steam generator. In addition, an adaptive neuro-fuzzy (ANFIS) self-tuning PID Controller for the nuclear core reactor is presented. This adaptive controller is used to enhance the performance characteristics of PWR by supporting the profile of the reactor power, the coolant fuel, and hot leg temperatures. The suggested proposed ANFIS self-tuning controller is estimated through a comparison with the conventional PID, neural network, and fuzzy self-tuning controllers. The results showed that the proposed controller is best over traditional PID, neural network, and fuzzy self-tuning controllers. All simulations are throughout by using MATLAB/SIMULINK.

Self-Tuned Fuzzy PID Temperature Controller Implementation in Hot-Air Coffee Roasting Process

Self-Tuned Fuzzy PID Temperature Controller Implementation in Hot-Air Coffee Roasting Process

Design and Evaluation of Self-Tuning Fuzzy PID Controller with Reference Model for Ball and Beam Mechanism

—Fuzzy Logic controller is one of the popular techniques in control system. However, the accuracy depends on numbers and the design of membership functions. Further, the output does not always fulfill specific design requirements. In this study, Fuzzy PID controller with first-order reference model had been designed and evaluated. The proposed new structure is able to guide fuzzy mechanism to produce desired output response as specified by the first-order model such as the time constant, rise time and settling time. The study was implemented on a ball and beam system to control the position of the ball while rolling on a beam. The control of a ball and beam system is challenging because it is an open-loop unstable system as the ball will continuously moves on the beam without control. The study also compares the results acquired from Fuzzy PID and Fuzzy PI plus PD against the proposed Fuzzy PID with first-order reference model controller. The evaluation covers for a pole starting from 0 until 1.0 for the first-order model. As a results, the proposed controller is able to produce an output response as specified where the fastest response was acquired when pole is set to 0.9. Whereas the Fuzzy PID and Fuzzy PI plus PD always produce the same transient response during step input test.

Experimental Investigations into the Behavior of Scaling Factors in a Fuzzy Logic Speed Control Induction Motor with Model Reference Adaptive Control

This paper presents a self-tuning fuzzy logic speed controller (FLSC) with model reference adaptive control (MRAC) for an induction motor (IM) drive system. The MRAC is examined by output scaling the factor tuner for optimum motor speed performance. A detailed investigation is carried out on the scaling factor control of the input change error and main FLSC output increment. This proposed method utilizes seven simplified rules of the 5 × 5 matrix membership functions to minimize the computational burden and memory space limitations. All simulation work is conducted using Simulink and Fuzzy Tools in the MATLAB software and the experimental testing with the aid of a digital signal controller board, dSPACE DS1103. Based on the results, the output scaling factor makes a more significant impact on the performance effect compared to the input error scaling factor. The input change error and output SF also exhibit similar behavior, indicating that a large range of UoD tuners works well in terms of capability load rejection while a small range of UoD tuners performs well in terms of rise time. The analysis includes no-load and load tests to ascertain the overshoot percentage, rise time, and settling time for transient and steady-state conditions.

Design and experimental implementation of DC-DC converter based self-tuning fuzzy PI controller

Bu çalışmada kendinden ayarlı bulanık PI (SFPI) kontrolör tabanlı DA-DA yükselten dönüştürücü topolojisi sunulmuştur. Dönüştürücünün kapalı çevrim transfer fonksiyonu elde edilmiş ve kararlılık analizi için küçük işaret modeli incelenerek tasarımı gerçekleştirilmiştir. Giriş-çıkış ilişkisi kara kutu modelleme ile doğrulanmıştır. Sistemin farklı anahtarlama frekanslarında 20 kHz ile 100 kHz aralığındaki davranışları incelenmiştir. Sistemin dinamik tepkisini test etmek için geleneksel PI kontrolör uygulanmış ve sistemde giriş gerilimi ve yük değişimleri gibi durumlarda gerilim regülasyonunun yetersiz olduğu gözlenmiştir. Bu nedenle dönüştürücünün çıkış gerilim regülasyonunu iyileştirmek için SFPI kontrolör önerilmiştir. Önerilen SFPI ile PI kontrolörler yükselme zamanı, yerleşme zamanı ve aşma gibi performans kriterleri ile karşılaştırılmıştır. Analiz sonuçlarına göre önerilen kontrolör ve sönümleme devrelerinin tasarlanması ile güç dönüşümündeki kayıpların azaltılarak sistem veriminin iyileştirildiği gözlenmiştir. Prototipi gerçekleştirilen dönüştürücünün nominal gerilimi 48 V, gücü 100 W olup sistemin genel verimi %92,9 olarak bulunmuştur. Sistemin analiz ve benzetim sonuçları deneysel sonuçlarla doğrulanmıştır.

Height control in GMA-AM using external wire as controlling variable

ABSTRACT Gas metal arc additive manufacturing (GMA-AM) is gaining great attention to produce large-scale parts owing to its merits of low device investments and high deposition rates. Despite this fact, a robust control system for deposition height in thin-walled parts is yet to be developed due to the lack of an appropriate controlling variable. This study attempts to propose an external wire-based GMA-AM technique to solve this issue. The influences of arc current, travel speed, and external wire feed speed (EWFS) on layer geometry were studied. It is indicated that the EWFS exhibits a greater influence on the layer height of thin walls than the arc current and the travel speed. The nozzle to the top surface distance (NTSD), monitored via a vision sensor, was extracted by Median filter, Sobel algorithm, Canny algorithm, and Hough transform. The dynamics between the NTSD and the EWFS were established via a nonlinear model. A parameter self-tuning fuzzy controller was designed to control the NTSD via changing the EWFS. System simulation tests and control tests were performed to validate the robustness of the parameter self-tuning fuzzy controller.

Self-tuning Fuzzy PID Controller Design and Energy Management in DC Microgrid: Standalone and Grid Connected Mode

This paper presents a control system and energy management strategy with a standalone and grid-connected mode in the microgrid. The microgrid is energized by distributed generation system of a photovoltaic panel, wind turbine and lithium-ion batteries. In this paper, the controller structure for regulating the voltage regulation in this microgrid consisting of renewable energy sources and battery system is covered. Overshoot, rise time, settling performances of designed self-tuning fuzzy proportional-integral-derivative controller and conventional proportional-integral-derivative controller examined comparatively. In addition, an energy management system has been proposed for loads which fed in the microgrid. Here, the aim is to make maximum use of renewable energy sources as much as possible, to maintaining voltage regulation and to ensure continuity in the feeding of the critical load. The system switches between the standalone and grid-connected modes if necessary. A model of the microgrid's dynamic behavior was constructed and it is simulated in the MATLAB®/Simulink environment. The results show that despite the changes in the production and load side of the grid, voltage and frequency oscillations are stabilized within a short time and allowed tolerance limits.

Investigation into the Electrohydraulic Synchronous Motion Control of a Thrust System for a Tunnel Boring Machine

The thrust system of a tunnel boring machine plays a crucial role by driving the machine ahead and supporting the gripper shoes stably. A thrust hydraulic control system, assembled with a proportional flow control valve and a pressure relief valve, is established with system operating parameters. The mathematical model of a thrust electrohydraulic system is presented. To improve the control characteristics of the thrust system, a self-tuning fuzzy PID controller was introduced in synchronization motion control situations. To attain the best control parameters, three synchronization motion control systems were used to control the thrust propel cylinders. Tests on a Ø2.5 m scaled TBM test rig were carried out to verify the capabilities of the ISCS, SRSCS and CRSCS. Comparative tests were conducted, and the results showed that the thrust system adopting SRSCS achieved the least oscillation and the quickest response. The steady-state displacement error decreased by about 33.3% in contrast to the ISCS and CRSCS.

Performance Analysis of Electro-Hydraulic Thrust System of TBM Based on Fuzzy PID Controller

The tunnel boring machine (TBM) is widely used in tunnel construction projects. The thrust system plays a crucial role to drive the machine ahead and support gripper shoes stably while tunneling. More and more attention has been paid to the pressure and velocity regulation efficiency as the TBM advances in complex rock conditions to ensure the stabilization of the tunneling process. A thrust hydraulic control system, assembled with a proportional pressure reducing valve, is established with system operating parameters. The mathematical model of the thrust electro-hydraulic system is revealed. To improve the control characteristics of the thrust system, a self-tuning fuzzy PID controller is introduced in the pressure and velocity regulation procedures. After that, tests on a Φ2.5 m scaled TBM test rig are carried out. The test results show that the thrust system adopting the fuzzy PID controller results in less oscillation and a smoother regulation process. It takes less time to reach the target goal of pressure regulation with less vibration during the pressure regenerating periods, and both systems of conventional PID controller and fuzzy PID controller are qualified in velocity regulation movements. The proposed control methods show better benefits in reduction of vibrations and shorter time of regulation to stable conditions, which extends the machine’s life and affects the acceleration of the tunneling process.

Modeling and Simulation of Two Axes Gimbal Using Fuzzy Control

The application of the guided missile seeker is to provide stability to the sensor's line of sight toward a target by isolating it from the missile motion and vibration. The main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling approach. The paper is started by deriving the mathematical model for gimbals motion using Newton's second law, followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/SimMechanics. Then, a Mamdani-type fuzzy and a Proportional-Integral-Derivative (PID) controllers were designed using MATLAB software. The performance of both controllers was evaluated and tested for different types of input shapes. The simulation results showed that self-tuning fuzzy controller provides better performance, since no overshoot, small steady-state error and small settling time compared to PID controller.

Self-tuning fuzzy PID speed controller for quarter electric vehicle driven by In-wheel BLDC motor and Pacejka's tire model

Vehicles using internal combustion engine (ICE) adversely affect the environment. Therefore, all vehicle makers have started their mutation toward electric vehicles. In this paper, a quarter vehicle model is developed considering brushless DC (BLDC) motor actuation. The longitudinal behavior of the drive wheel is modeled using the Pacejka model or magic formula. This model describes the interaction between the tire and the road surface, which reflects road condition effect. The purpose of the control is to provide smooth speed control in both acceleration and deceleration modes, regardless of changes in road conditions. Due to the high complexity of the control model, a hybrid fuzzy-PID controller is designed to serve as a speed controller for the system motor-tire-road-surface. It is checked by numerous simulations that the proposed controller performs well, ensuring comfortable steering operation despite road characteristic changes.