Self-tuning Fuzzy Research Articles

Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system

In the realm of Educational Technology, personalized learning is pivotal, yet predicting students' learning abilities based on learning styles and ICT remains challenging. We propose a decision support system using Machine Learning (ML), swarm intelligence, and explainable artificial (XAI) techniques to assess students' performance. Our model employs Chaotic Particle Swarm Optimization (C-PSO) with Henon execution, outperforming Genetic Algorithm (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Bee Colony Optimization (BCO), Artificial Fish Swarm Algorithm (AFSA), Mayfly Optimization Algorithm (MFOA), Mother Optimization Algorithm (MOA), Fuzzy Self-Tuning PSO (FST-PSO). Evaluating efficiency, effectiveness, and solution quality reveals C-PSO's superiority. The study identifies the significant impact of ICT on self-progress and employs Spearman Rank correlation for statistical validation. Findings suggest C-PSO as an effective tool for optimizing educational data analysis and decision-making. Further exploration in real-world educational settings and comparative analyses with alternative optimization techniques are recommended for future research.

Improvement of ride quality for a wheel loader with semi-active cab isolation system via fuzzy self tuning of PID controller

Improving driver ride comfort of a wheel loader is important to avoid potential health hazards from the whole-body vibration. A fuzzy self-tuning of PID controller is designed to control the damping coefficient of Semi-active cab isolation system (SCIS) for a wheel loader. A dynamic model of a wheel loader is establish under survey conditions for analysis and evaluation. The r.m.s acceleration responses of the vertical driver's seat and pitch angle of vehicle body according to ISO2631-1 (1997-E) are chosen as objective functions. The effectiveness of the proposed control strategy is analyzed through simulations involving excitations for a random road profile in time domain. The proposed SCIS is simulated and analyzed by Matlab/Simulink software in comparison with a passive cab isolation system (PCIS) under survey conditions. The achieved results indicate that ride comfort effectiveness of a wheel loader with SCIS is better than PCIS under survey conditions. In addition, the study results are the initial basis for optimizing controller parameters.

Optimum PID Controller with Fuzzy Self-Tuning for DC Servo Motor

DC motors are simple and controllable, making them a popular choice for various applications. However, the speed and load characteristics of DC motors can change, making it difficult to control them effectively. This paper proposes an optimum PID controller with fuzzy self-tuning for DC servo motors. The controller uses two steps to adjust the PID gains: The ACS algorithm is employed to identify the optimal PID gains in the first step. A fuzzy logic (FLC) controller is employed in the second stage to further fine-tune the gains. The FLC considers two cost functions: the first function is the sum of the squares of the error between the controlled output and reference input. The second function is a mathematical expression that specifies the required characteristics of the system response. The fuzzy self-tune then uses a set of rules to adjust the PID gains in response to changes in the system. The rules are based on the two cost functions designed to maintain the optimum PID gains for various operating settings. The outcomes of the two functions are: Kp = 5.2381, Ki = 7.0427, and Kd = 0.49468, with rising time = 0.2503, overshoot = 2.5079, and settling time = 10.4824 in the first cost function. The second cost function outcomes are Kp = 8.1381; Ki = 8.6427; and Kd = 0.49468. The FST-PID controller's performance is evaluated using Matlab-Simulink. The proposed controller was tested on a DC servo motor, and the results showed good performance in both steady-state and transient responses. The controller also maintained the optimum PID gains in the event of changes or disturbances. So, the motor's speed can effectively control under a variety of conditions.

A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration

Some optimization problems are difficult to solve due to a considerable number of local optima, which may result in premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach coupled with a self-tuning fitness function. More specifically, to evaluate the fitness of the produced surrogate functions, we employ Fuzzy Self-Tuning Particle Swarm Optimization, a setting-free version of particle swarm optimization. To assess the performance of the proposed method, we considered a set of benchmark functions characterized by high noise and ruggedness. Moreover, the method is evaluated over different problems’ dimensionalities. The proposed approach reveals its suitability for performing the proposed task. In particular, experimental results confirm its capability to find the global argminimum for all the considered benchmark problems and all the domain dimensions taken into account, thus providing an innovative and promising strategy for dealing with challenging optimization problems. Doi: 10.28991/ESJ-2023-07-04-01 Full Text: PDF

Satellite Tracking Control System Using Optimal Variable Coefficients Controllers Based on Evolutionary Optimization Techniques

Satellite tracking control system is mechanism that redirects the parabolic antenna to the chosen satellite automatically. It perfectly tracks the satellite as it spins across the sky in its orbit. To maintain a continuous communication signal throughout multiple satellite tracking missions, the tracking process must be fast and smooth, with minimal deviations from the target position. Various controller models have been presented over time to address the problem of antenna positioning in satellite systems and to track moveable targets using servomechanism. The purpose of this study is to describe and debate a satellite tracking control system based on a DC servo motor. For optimal tuning of Proportional-Integral-Derivative (PID), Fractional Order PID (FOPID) and Variable Coefficient Fractional Order PID (V-FOPID) controllers that were used in satellite control system, Particle Swarm Optimization (PSO), Gravitational Search Algorithm with Particle Swarm Optimization (GSA-PSO) and Eagle Strategy with Particle Swarm Optimization (ES-PSO) techniques were proposed. Dynamic Performance Indices Based Objective Functions is used to compute the Performance Index. Furthermore, Self-Tuning Fuzzy FOPID (STF-FOPID) is proposed for satellite tracking control system. The system's response is analyzed, and the outcomes of various control strategies are measured and compared to others. The obtained results implies that Variable Coefficient Fractional Order PID controller tuned using Eagle Strategy with Particle Swarm Optimization can precisely trace the desired position with the fastest settling time and free overshoot when compared to other control strategies.

Fractional-Order Active Disturbance Rejection Control with Fuzzy Self-Tuning for Precision Stabilized Platform.

In this paper, a novel fractional-order active disturbance rejection control with fuzzy self-tuning method (FSFOADRC) is proposed for photoelectric tracking system (PTS). Firstly, aiming at the internal uncertainty of PTS and external disturbance, a fraction-order extended state observer (FOESO) is designed, and the FOESO can transform the plant into a simple form, which greatly simplifies the mathematical model. Secondly, a fuzzy regulator is applied to the proportion-differentiation controller (PD), increasing the flexibility and adaptivity of the controller. In addition, the stability of the whole control system can be guaranteed. Eventually, numerical comparative simulations are implemented to verify the feasibility and superiority of the proposed method. Compared with the integral-order active disturbance rejection control (IOADRC), fractional-order active disturbance rejection control (FOADRC) without the fuzzy regulator and proportion-integration-differentiation (PID) controller, the proposed method performs better with faster response, smaller overshoot, and stronger disturbance suppression capability.

Sun Flower Optimization with Self-Tuned Fuzzy Logic MPPT Controller and Reactive Power Compensation for Grid-Connected PV System

This paper presents a detailed study on the analysis of the reactive power compensation methodology used for delivering the maximum power to single-phase grid–PV systems. The main purpose of this work is to develop a Sunflower Optimization (SFO) integrated with a Self-tuned Fuzzy Logic Controller (SFLC)-based Maximum Peak Point Tracking (MPPT) controlling mechanism for extracting the maximum power from the solar PV system. For this, an efficient Entwined LUO (ELUO) DC-DC converter has been utilized that helps to regulate the output voltage with reduced ripples. Also, the inverter provides real power in addition to the reactive power without exceeding its current limit. For controlling the capability of energy transmission, the Phased Locked Loop (PLL) based synchronization is performed at the point of common coupling. Additionally, the LCL filtering technique is employed to reduce the harmonics and unbalanced current efficiently. The proposed system improves the overall system performance by producing grid-side current in a clean sinusoidal shape with an appropriate phase shift in grid voltage. The simulation and experimental results are validated to demonstrate the effectiveness of the proposed control method.

Optimal Control Strategy of Back-to-Back Converter Based on AC/DC Voltage Source Converter

The back-to-back converter is a converter system composed of two voltage source converters (VSC). Because VSC can regulate the output of active and reactive power, the back-to-back converter has a good dynamic and static performance. However, not all passive loads are three-phase symmetrical. The existing control strategy may lead to asymmetric output voltage when back-to-back converter is used to supply unbalance load. Usually, an inner loop d/q decoupling controller, a constant DC voltage controller of the rectifier side, and a constant AC voltage controller of the inverter side are established. In this article, in order to improve the rectifier side, fuzzy online self-tuning is used to increase PI parameters on the outer voltage loop. PI control is replaced by PR control on the inner current loop. The improved rectifier control can reduce the overshoot and the output of harmonics. For the inverter side, the three-phase voltage will be imbalanced. Therefore, using PR control, which can track a sinusoidal signal without steady-state error, the control strategy of inverter is changed into “outer loop constant voltage and PR control-inner loop current d/q decoupling control.” This strategy can solve the unbalance problem of three-phase AC output. The simulation results show that the control strategy can solve the problem of asymmetric output voltage of the inverter side effectively.

Evaluation of Ride Comfort for Active Suspension System Based on Self-tuning Fuzzy Sliding Mode Control

Evaluation of Ride Comfort for Active Suspension System Based on Self-tuning Fuzzy Sliding Mode Control

DESIGN OF ADAPTIVE FUZZY FRACTIONAL ORDER PID CONTROLLER FOR AUTONOMOUS UNDERWATER VEHICLE (AUV) IN HEADING AND DEPTH ATTITUDES

This paper deals with the design of new self-tuning Fuzzy Fractional Order PID (AFFOPID) controller based on nonlinear MIMO structure for an AUV in order to enhance the performance in both transient state and steady state of traditional PID controller. It is particularly advantageous when the effects of highly nonlinear processes, like high maneuver, parameters variation, have to be controlled in presence of sensor noises and wave disturbances. Aspects of AUV controlling are crucial because of Complexity and highly coupled dynamics, time variety and difficulty in hydrodynamic modeling. In this try, the comprehensive nonlinear model of AUV is derived through kinematics and dynamic equations. The scaling factor of the proposed AFFOPID Controller is adjusted online at different underwater conditions. Combination of adaptive fuzzy methods and PID controllers can enhance solving the uncertainty challenge in the PID parameters and AUV parameter uncertainty. The simulation results show that developed control system is stable, competent and efficient enough to control the AUV in path following with stabilized and controlled speed. Obtained results demonstrate that the proposed controller has good performance and significant robust stability in comparison to traditional tuned PID controllers.

Inverter current control for reactive power compensation in solar grid system using Self-Tuned Fuzzy Logic Controller

The solar photovoltaic (PV) systems have gained more attention in renewable energy production due to their cost efficiency and reliability. Typically, reactive power compensation and harmonics elimination are challenging and demanding tasks for improving the efficacy of grid-connected solar PV systems. For this purpose, many research works developed different converter and controller topologies for solving the power quality issues in grid-PV systems. But, it limits the problems of increased harmonics, computation complexity, inefficiency and reduced performance outcomes. Thus, this research aims to develop an integrated hysteresis current controller and Self-Tuned Fuzzy Logic (SFLC) based MPPT controllers for eliminating the harmonics and unbalanced current in single-phase grid systems. Also, it helps to extract the maximum amount of power from the solar PV array. The LUO converter is deployed to reduce ripple contents. The Phased Locked Loop (PLL) based synchronization is performed to maintain the phase angle, frequency and magnitude levels of source power. Moreover, the hysteresis current controller for the inverter has been specifically designed to reduce the THD of the system under IEEE519_1992 regulations. During experiments, both the simulation and hardware results have been evaluated for validating the performance of the proposed controller design.

Efficient Self-Tuned Fuzzy Logic Based Power System Stabilizer

A mechanism for efficient self-tuning of a fuzzy logic-based PSS in real-time is presented. The proposed controller with a self-tuning mechanism can reduce low-frequency oscillations regardless of magnitude or type of disturbance, whether sudden or gradual. It is not necessary to set the initialization or learning factor for each disturbance and its implementation is simple. Speed deviation and its derivative are used as fuzzy controller inputs. The main idea, based on the properties of the conventional PID controller, is to use a fuzzy controller similar to a PID controller but for the nonlinear power system. Since the objective is implementation on a physical system, nonlinearity without any simplification of the system is very important. Therefore, in this mechanism, the full order machine model is considered. The optimal performance is obtained by determining in real-time the appropriate gains for each input. Tests for a wide range of disturbances show the robustness of the controller. The proposed approach has fewer fuzzy rules than similar methods. Therefore, its hardware implementation can be simpler and will require less computation time. Very good damping of all types of disturbances is a prominent feature of this method.

A Novel Self-Tuning Fuzzy Logic-Based PID Controllers for Two-Axis Gimbal Stabilization in a Missile Seeker

Tracking a target is an essential function of a seeker for missiles. The target tracking mechanism of a seeker consists of gimbals, mounted with gyroscopes, and an antenna or some other energy receiving devices such as radar, infrared (IR), or laser. Stabilization of such a gimbal is necessary for any guided missile to maintain the tracking device always pointing towards the target. For the stabilization of the gimbal system, several control methods have been employed for making the gimbal to follow an input rate command by eliminating all the gimbal disturbances. Here, a new self-tuning fuzzy logic-based proportional, integral, derivative (PID) controller is introduced for the stabilization of a two-axis gimbal for a manoeuvring guided missile. The proposed control method involves tuning the gains of the PID controller based on the fuzzy logic rule bases considering the missile body rotation. The performance of the stabilization loops has been verified through MATLAB simulations for fuzzy logic-based PID controller compared with the conventional PID controller. The simulation results show the response of the gimbal system with stabilization loops met the control requirements with fuzzy PID controllers but not with conventional PID controllers.

Analytical Study of the Robustness of the Different Variants of Fractional-Order Self-Tuned Fuzzy Logic Controllers

Due to the evolution in technologies, it has become necessary for the systems to work in real time and for efficient output they have to be adaptive. Since the conventional Proportional-Integral-Derivative (PID) controller lacks to control the less informative non-linear systems and has fixed parameters, therefore, artificial techniques with the inclusion of adaptability has been studied in this paper. It has been observed that by implementing an extra layer of fuzzy controller in parallel, the gains of the controller could be change in real time and can highly reduce the external effects on the system as compared to the non-adaptive controller. In addition to this, different variants of fuzzy controllers like self-tuned fractional-order fuzzy PD (STFOFPD), self-tuned fractional-order fuzzy PI (STFOFPI) and self-tuned fractional-order fuzzy PID (STFOFPID) are compared and it was found that the STFOFPID outperforms all the controllers in the servo-regulatory mechanism.

Distributed Formation Control Using Fuzzy Self-Tuning of Strictly Negative Imaginary Consensus Controllers in Aerial Robotics

Wind gusts are significant barriers to the outdoor operations of networked multiple unmanned aerial vehicles (UAVs), which fly in close proximity to each other or around obstacles. As such, traditional control methods such as PID control may not perform adequately. Based on the strictly negative imaginary (SNI) systems theory, this article presents a novel decentralized and adaptive consensus-based formation control law that drives multiple UAVs to follow the desired formation in the presence of limited bandwidth for information exchange and dynamically changing environmental conditions. To be consistent with a decentralized approach, each UAV only measures its relative position with respect to its neighbors according to a fixed information graph. As a result, the required formation is obtained by maintaining the desired relative positions among UAVs. Moreover, to deal with the challenging dynamics of flight environments, we also employ a knowledge-based fuzzy inference system to automatically adjust the parameters of the SNI consensus controllers, leading to the development of a fast and robust adaption method. In this article, we conduct a stability analysis based on the SNI theorem and rigorously compare the performance of our controllers with respect to the performance of conventional PID controllers. The efficacy of the overall closed loop control system is highlighted in real-time flight tests.

Self-Tuning Fuzzy Task Space Controller for Puma 560 Robot

Since in most robot applications the desired paths are determined in task space or Cartesian space, it is important to control the robot arm in task space. In this paper, a fuzzy controller with modifiable scaling factors is proposed to control the robot end-effector in task space. The controller is a fuzzy system with a mechanism to change the scaling factors when the error is bounded under a predetermined value. The controller is designed in joint space and is developed to work space by using inverse Jacobian strategy. The simulations results on Puma 560 robot manipulator illustrate the high performance of the presented control method.

Fuzzy Self-Tuning of Strictly Negative-Imaginary Controllers for Trajectory Tracking of a Quadcopter Unmanned Aerial Vehicle

Robustness in the face of uncertainties is an integral part of designing a real-time control system. Based on negative imaginary (NI) systems theory, we design robust and adaptive control systems for accurate trajectory tracking of a quadcopter aerial vehicle. Considering the challenging dynamics of unmanned aerial vehicles, we employ knowledge-based fuzzy inference systems (FIS) to facilitate automatic tuning in our SNI controllers, leading to the development of adaptive SNI control systems. Unlike fixed-gain controllers that have no ability to adapt to the variations in environmental conditions or changes in the dynamics of the plant, our adaptive SNI controllers are able to perform self-tuning to constantly update their parameters. The concept of adaptive autopilots will enhance the ability of the closed-loop control systems to accommodate large uncertainties. To demonstrate their efficacy, we design and implement our adaptive SNI controllers in the three-position control loops of the AR.Drone quadcopter after conducting extensive computer simulations. We also perform a rigorous comparative study with respect to the performance of fixed-gain SNI controllers, fixed-gain NI systems, in addition to model-predictive-control systems, and proportional integral derivative (PID) control systems as our benchmarks. To complete the study, we conduct a stability analysis based on Kharitonov's Theorem.

DSP‐based comparison of PFC control techniques applied on bridgeless converter

A comparison of fuzzy self-tuning (FST), sliding mode control (SMC) and conventional proportional-integral (PI) control methods is performed under unbalanced grid conditions using a bridgeless power factor correction (PFC) converter. The bridgeless PFC converter operates the correction of AC line current to obtain a DC output voltage without full bridge rectifier based on these control methods. These control methods generate the duty cycles to provide higher unity power factor and lower total harmonic distortion of input current, even if AC line voltage is distorted. This study focuses on the performance analysis of FST and SMC methods adaptation to the input current for the bridgeless PFC converter and comparing with the conventional PI controller. Although the SMC method is only used in the current control loop, the FST method is used in both current and voltage control loops for eliminating harmonics of input current and regulating of output voltage. The performance of control methods is evaluated with a TMS320F2812 digital signal processor. The simulation and experimental results are compatible with the limits for harmonic current emissions, IEC 61000-3-2 and show that the FST and SMC are better than the conventional PI control method and FST is superior to the SMC.

Type-2 fuzzy self-tuning of modified fractional-order PID based on Takagi\u2013Sugeno method

In this paper, Takagi–Sugeno (TS) fuzzy technique is combined with interval type-2 fuzzy sets (IT2-FSs) to design a new adaptive self-tuning fractional-order PID (FOPID) controller. TS fuzzy technique is used to construct a modified FOPID controller (TSMFOPID). IT2-FSs are used as a tuner for TSMFOPID to update their gains under parameter uncertainty change and to compensate the controlled system undesired effects of the sudden disturbances. Three types of IT2-FSs tuning methods are used for TSMFOPID. The first one is to tune the proportional–integral–derivative gains of the TSMFOPID. The second one deals with tuning the fractional orders of integral and derivative effects. The last one tunes the five parameters of the TSMFOPID. The responses of the fractional orders of the integral and derivative actions on the controlled system driven by TSMFOPID are given and discussed. The three tuning methods via IT2FST for TSMFOPID controller are applied to load frequency control as a case study of a power system comprising a single area. Comparative studies of the tuning type-1 fuzzy sets (T1FSTs) and IT2FST methods for controlled system using TSMFOPID are carried out and the results are discussed. The results prove that the proposed IT2FST for TSMFOPID controller is very useful for considered application over disturbance changes and parameter uncertainties.

Practical interval type-2 fuzzy self-tuning of PID controller to servo permanent magnet synchronous motor

This paper implements a practical interval type-2 fuzzy self-tuning (IT2FST) of optimal PID (OPID) controller to servo permanent magnet synchronous motor (SPMSM). The proposed method IT2FST updates the OPID controller gains in an online manner to drive the SPMSM with better speed response during variable load and parameter uncertainty occurrence. In this work, the industrial SPMSM system comprises three-phase PMSM with internal break, drive and mechanical parts. Due to the incomplete real information of the SPMSM, nonlinear least square algorithm has been utilized for its model identification. A comparative analysis in a real time of the SPMSM with an OPID, type-1 fuzzy self-tuning and IT2FST for OPID controllers under the influence of parameter uncertainties and external load disturbances has been carried out. The real-time practical implementation results illustrated that the proposed IT2FST of OPID controller gives a simple opportunity to enhance the speed performance of the SPMSM than the other controllers.