Type-2 Fuzzy Logic Research Articles

Adaptive Integral Sliding Mode Control with Chattering Elimination Considering the Actuator Faults and External Disturbances for Trajectory Tracking of 4Y Octocopter Aircraft

This paper presents a control strategy for a 4Y octocopter aircraft that is influenced by multiple actuator faults and external disturbances. The approach relies on a disturbance observer, adaptive type-2 fuzzy sliding mode control scheme, and type-1 fuzzy inference system. The proposed control approach is distinct from other tactics for controlling unmanned aerial vehicles because it can simultaneously compensate for actuator faults and external disturbances. The suggested control technique incorporates adaptive control parameters in both continuous and discontinuous control components. This enables the production of appropriate control signals to manage actuator faults and parametric uncertainties without relying only on the robust discontinuous control approach of sliding mode control. Additionally, a type-1 fuzzy logic system is used to build a fuzzy hitting control law to eliminate the occurrence of chattering phenomena on the integral sliding mode control. In addition, in order to keep the discontinuous control gain in sliding mode control at a small value, a nonlinear disturbance observer is constructed and integrated to mitigate the influence of external disturbances. Moreover, stability analysis of the proposed control method using Lyapunov theory showcases its potential to uphold system tracking performance and minimize tracking errors under specified conditions. The simulation results demonstrate that the proposed control strategy can significantly reduce the chattering effect and provide accurate trajectory tracking in the presence of actuator faults. Furthermore, the efficacy of the recommended control strategy is shown by comparative simulation results of 4Y octocopter under different failing and uncertain settings.

Innovative fuzzy logic type 3 controller for transient and maximum power point tracking in hydrogen fuel cells

This study introduces an advanced Fuzzy Logic Type 3 (FLT3) controller for Proton Exchange Membrane Fuel Cells (PEMFCs), aiming to optimize Maximum Power Point Tracking (MPPT) under dynamically varying temperature conditions. The FLT3 controller advances fuzzy logic control by incorporating an additional layer of uncertainty modelling, which enhances its ability to manage the inherent nonlinearities in PEMFC systems, such as temperature fluctuations and gas pressure variations. The controller is developed and validated through MATLAB/Simulink simulations, using a refined rule based on mechanism with finely tuned membership functions for precise adaptation to changing conditions. When compared to traditional Perturb and Observe (P&O) and Fuzzy Logic Type 2 (FLT2) controllers, the FLT3 controller demonstrates superior performance by achieving faster sitting times, minimizing power fluctuations, and maintaining operation closer to the nominal power point, particularly under transient conditions for hydrogen-based fuel cell. This innovative approach significantly improves the efficiency and stability of PEMFC systems, marking a substantial advancement in their operational management.

Event-Triggered Fuzzy Adaptive Predefined-Time Control for Fractional-Order Nonlinear Systems with Time-Varying Deferred Constraints and Its Application

This paper focuses on the fuzzy adaptive predefined-time control for fractional-order nonlinear systems with time-varying deferred constraints. First, a modified dynamic surface control technique is introduced to address the problem of computational complexity exposed in the backstepping framework, and the interval type-2 fuzzy logic systems are applied to model the unknown nonlinearities of the systems. Next, a shifting function and the barrier Lyapunov function with variational barrier bounds are formulated to deal with the constraints issue. Particularly, the constraint conditions can be satisfied within a predetermined time, even if they are transgressed initially. Furthermore, a switching threshold event-triggered controller is devised to balance the control energy and communication resources. With the help of the predefined-time stability criterion, it is proven that the presented predefined-time event-triggered controller can ensure that all the signals involved in the closed-loop system are bounded and the tracking error fluctuates to a small neighborhood of the origin in a predefined-time interval. Finally, two simulation examples are provided to confirm the effectiveness of the put-forward control algorithm.

UAV Trajectory Tracking Using Proportional-Integral-Derivative-Type-2 Fuzzy Logic Controller with Genetic Algorithm Parameter Tuning.

Unmanned Aerial Vehicle (UAV)-type Quadrotors are highly nonlinear systems that are difficult to control and stabilize outdoors, especially in a windy environment. Many algorithms have been proposed to solve the problem of trajectory tracking using UAVs. However, current control systems face significant hurdles, such as parameter uncertainties, modeling errors, and challenges in windy environments. Sensitivity to parameter variations may lead to performance degradation or instability. Modeling errors arise from simplifications, causing disparities between assumed and actual behavior. Classical controls may lack adaptability to dynamic changes, necessitating adaptive strategies. Limited robustness in handling uncertainties can result in suboptimal performance. Windy environments introduce disturbances, impacting system dynamics and precision. The complexity of wind modeling demands advanced estimation and compensation strategies. Tuning challenges may necessitate frequent adjustments, posing practical limitations. Researchers have explored advanced control paradigms, including robust, adaptive, and predictive control, aiming to enhance system performance amidst uncertainties in a scientifically rigorous manner. Our approach does not require knowledge of UAVs and noise models. Furthermore, the use of the Type-2 controller makes our approach robust in the face of uncertainties. The effectiveness of the proposed approach is clear from the obtained results. In this paper, robust and optimal controllers are proposed, validated, and compared on a quadrotor navigating an outdoor environment. First, a Type-2 Fuzzy Logic Controller (FLC) combined with a PID is compared to a Type-1 FLC and Backstepping controller. Second, a Genetic Algorithm (GA) is proposed to provide the optimal PID-Type-2 FLC tuning. The Backstepping, PID-Type-1 FLC, and PID-Type-2 FLC with GA optimization are validated and evaluated with real scenarios in a windy environment. Deep robustness analysis, including error modeling, parameter uncertainties, and actuator faults, is considered. The obtained results clearly show the robustness of the optimal PID-Type-2 FLC compared to the Backstepping and PID-Type-1 FLC controllers. These results are confirmed by the numerical index of each controller compared to the PID-type-2 FLC, with 12% for the Backstepping controller and 51% for the PID-Type-1 FLC.

Adaptive hierarchical interval type-2 fuzzy control for trajectory tracking of an underactuated AUV with uncertain dynamics

In this study, an adaptive hierarchical interval type-2 fuzzy control (AHIT2FC) scheme is presented for three-dimensional (3D) trajectory tracking control of an underactuated autonomous underwater vehicle (AUV), which has only three independent control inputs and is subject to partially parametric uncertainty and unknown external disturbances. To decrease reliance on hydrodynamic parameters, an interval type-2 fuzzy logic system (IT2FLS) is utilized, which exhibits superior capabilities in handling high levels of uncertainties prevalent in the underwater environment as compared to a type-1 fuzzy logic system. However, the traditional fuzzy system structure encounters the “curse of dimensionality” issue, which results in high computational complexity and imposes a significant burden on the limited computing resources underwater. The proposed AHIT2FC framework, based on a hierarchical structure, can substantially enhance computational speed while ensuring excellent control performance. Subsequently, Lyapunov’s theory is adopted to guarantee that all the tracking errors in the closed-loop system are semiglobally uniformly bounded (SGUB). Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed AHIT2FC scheme.

Classification of high-resolution remote sensing images based on interval type-2 fuzzy logic system

Abstract Aiming at the complexity and uncertainty of remote sensing image classification, this paper proposes a high-resolution remote sensing image classification method based on Interval type-2 Fuzzy Logic Systems for Remote Sensing (IT2FLS-RS), which focuses on dealing with multiple uncertainties and aims to improve the accuracy and reliability of remote sensing image classification. The method establishes a more complex interval two-type fuzzy system covering two types of fuzzifiers, a rule base and an inference machine, and finally uses an integrated algorithm as a defuzzifier to achieve accurate pixel-level classification. In addition, the model uses Constrained Optimization BY Linear Approximations (COBYLA) to optimise the key parameters. In the DLRSD dataset, the accuracy of this model is improved by about 20%, 14%, 24% and 10% compared to the state-of-the-art interval two-type fuzzy neural network algorithm and the benchmark model XGBoost, respectively. On the WHDLD dataset, the accuracy of the proposed method is improved by about 12% and 10% compared to the state-of-the-art interval type-2 fuzzy neural network algorithm and the benchmark model XGBoost, respectively. The experimental results confirm the robustness of the proposed method in processing high-resolution remote sensing image classification, especially the excellent adaptability and scalability in complex feature scenes.

Critical study of type-2 fuzzy logic control from theory to applications: A state-of-the-art comprehensive survey

The review systematically explores the theoretical foundations of type-2 fuzzy sets and the associated mathematical frameworks of the advancements in type-2 fuzzy logic systems (T2-FLS), as well as a comprehensive overview of the state-of-the-art techniques and applications of type-2 fuzzy logic systems. The review's application-oriented section looks at a number of different areas where type-2 fuzzy logic has made a big difference. These areas include robotics, autonomous vehicle control, electric and electronic control, image processing, and railway control. This overview mainly discusses the past, present, and future trends in type-2 fuzzy logic applications. The primary contribution summarizes the most essential type-2 fuzzy logic research with theoretical and practical implications. This article will provide a critical path and a strong foundation for individuals interested in this field to pursue future research.

Robust control based on type-2 fuzzy logic for permanent magnet synchronous motor (PMSM)

This paper introduces a novel control technique utilizing the orientation of stator flux based on type 2 fuzzy controllers to control the mechanical power generated by a permanent magnet synchronous motor and addressing parametric variation challenges to enhance robust performance against external fluctuations. The Permanent Magnet Synchronous Motors driven by stator variables via two converters relies on these components to interface with the electrical network in distinct manners. The network-side converter facilitates continuous bus control and enhances power factor, while the motor-side converter enables precise control and optimization of energy flow during motor operation. The paper outlines the motor and converter models, followed by the development of control commands to regulate mechanical power (speed, torque) for optimal efficiency and production quality and we will then present a comparative study between the developed controls to highlight the best performing and most robust of each, . Numerical simulations demonstrate the effectiveness of implementing type 2 fuzzy logic control in achieving these objectives.

Power Quality Enhancement in High-Voltage Transmission Systems Using STATCOM by Type 1 and Type 2 Fuzzy Logic Controllers

The continuing growth of load demand leads to increased power system complexity and interconnections. During faults and abnormal operating conditions, conventional power systems change the load flow in transmission lines, which may cause voltage drops or swells, and other power quality issues. Voltage regulation is connected to reactive power compensation in system busbars, and fixed capacitor banks are usually used for this purpose, but they are not flexible and dynamic enough to meet the power quality requirements. With power electronics development, flexible alternative current transmission systems (FACTS) became a popular solution for power quality improvement and power transmission capability increment in power systems, and FACTS can be categorized into series and shunt systems. In this study, STATCOM was used to improve the voltage profile in part of Iraq’s high-voltage transmission system during abnormal operating conditions, such as sudden load addition and removal, which causes voltage dip and voltage swell. Type 1 fuzzy logic controller and type 2 fuzzy logic controller were used as the outer controller in the STATCOM; both controllers were able to regulate the voltage magnitude by reducing the voltage dip from 1.32 to 0.4%, voltage swell from 1.02 to 0.5%, and voltage THD at the connecting point did not exceed 3%, indicating that SATACOM with FLC improves the voltage profile under different operating conditions.

New Type-2-Fuzzy-Logic-Based Control System for the Cessna Citation X

This paper presents a new control law based on type-2 fuzzy logic system (T2FLS) for the Cessna Citation X aircraft during cruise. This methodology combines three control systems: a T2FLS, a sliding mode control (SMC) system, and an adaptive control system. Differing from conventional controllers, this control system can deal with uncertainties and aircraft dynamics variations using the T2FLS approximator. The approximated functions were updated during the simulation iterations using adaptation laws derived from the Lyapunov theorem. The adaptability characteristic of this controller allows updating the approximated functions while the other design parameters remain unchanged. This fact simplifies the controller design process and eliminates the need to use a group of conventional controllers with different gains to control the aircraft in various flight conditions. In addition, the sliding mode control system helps to guarantee the aircraft’s stability and robustness, along with two other methodologies in the presence and absence of turbulence. The performance of this control methodology was validated with a nonlinear simulation platform developed for the Cessna Citation X business aircraft using accurate flight data derived from a Level D flight simulator at the Research Laboratory in Active Controls, Avionics and AeroServoElasticity (LARCASE).

Impedance interaction and power flow enhancement in DC microgrids by using interval type-2 fuzzy logic and active voltage stabilizer-based hybrid damping controller

Impedance interaction and power flow enhancement in DC microgrids by using interval type-2 fuzzy logic and active voltage stabilizer-based hybrid damping controller

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

A practical design of interval type-2 fuzzy logic systems with application to solar radiation prediction

A practical design of interval type-2 fuzzy logic systems with application to solar radiation prediction

Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN's goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future.

Advanced seismic control strategies for smart base isolation buildings utilizing active tendon and MR dampers

This paper investigates the seismic behaviour of a five-storey shear building that incorporates a base isolation system. Initially, the study considers passive base isolation and employs a multi-objective archived-based whale optimization algorithm called MAWOA to optimize the parameters of base isolation. Subsequently, a novel model is proposed, which incorporates an interval type-2 Takagi-Sugeno fuzzy logic controller (IT2TSFLC) utilizing clustering techniques. The building includes Magneto-rheological (MR) dampers installed at the base isolation level and two active tendons positioned on the first and second storeys of the structure. The semi-active control force of the base isolation with MR dampers is determined by the fuzzy system, while the active control force for the active tendons is computed using the linear quadratic regulator (LQR) algorithm, enabling control force provision during seismic events. The primary objective of this model is to enhance the seismic control of the building. Therefore, it is classified as a proposed model. The structural system is subjected to seismic analyses, considering three different structural configurations: uncontrolled, equipped with passive base isolation, and equipped with semi-active base isolation combining MR dampers and active tendons. The findings of the research demonstrate that by considering the optimization of parameters of the passive base isolation based on the white noise scenario and using these parameters as design parameters, during seismic analysis of the structure in some earthquakes, increased structural responses were observed when compared to uncontrolled structure, highlighting a potential risk. Nevertheless, the proposed system effectively addresses this drawback of passive control systems by markedly reducing structural responses, as compared to both passive base isolation and uncontrolled structure. These results suggest that the proposed system is an effective solution for mitigating seismic risks in structural seismic control.

Dominant hippocampus segmentation with brain atrophy analysis-based AD subtype classification using KLW-RU-Net and T1FL

A progressive neurodegenerative disorder that leads to cognitive decline and memory loss is known as Alzheimer's Disease (AD). Researchers propose an integrated framework centered on brain atrophy analysis and hippocampus segmentation to predict AD and its subtypes. This approach augments the accurate prediction of AD subtype classification, thus addressing the lack of emphasis in prevailing works. Primarily, the T1-weighted brain Magnetic Resonance Imaging (MRI) images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database undergo preprocessing. After that, for spatiotemporal evaluation, region segmentation, tau accumulation analysis, and Feature Extraction (FE), the image is processed. By employing the Fisher-Kolmogorov (FK) model, the essential parameters are extracted in spatio-temporal evaluation. In the meantime, for tissue segmentation, the Knowledge Partitioned Clustering (KPC) is utilized, and for hippocampus segmentation, the Kullback-Leibler Within-layer Regularized UNet (KLW-RU-Net) is employed. Moreover, by binarizing the image, the tau accumulation is analyzed; in addition, Principal Component Analysis (PCA) is utilized for FE. After PCA, the Adaptive Step-sized Gauss Rabbits Optimization (ASGRO) is employed for feature selection. Subsequently, to classify AD, the Dilated Derivative Sigmoid-Weighted DenseNet (DD-SWDN) is employed. Additionally, by employing Type-1 Fuzzy Logic (T1FL), the subtype of AD is classified, thus achieving higher accuracy for AD subtype classification. Therefore, as per these outcomes, the proposed work performs better than the other conventional approaches.

A novel type-2 decision mechanism for dynamic parameter adaptation: theory and application in mathematical and structural problems

Metaheuristic algorithms are stochastic-based search techniques widely used for solving different types of optimization problems. These methods mostly adjust their search behavior using pre-defined search pattern(s) regardless of the current problem specifications. Therefore, integrating them with logical auxiliary modules can significantly enhance their search efficiency by enabling them to dynamically adapt their search behavior. The present study introduces a novel decision-making approach that employs interval type-2 fuzzy logic to balance the search behavior during optimization process. The proposed approach, designed as a stand-alone module with the flexibility to be integrated into various algorithms, is incorporated into the Interactive Search Algorithm. The developed reinforced technique is named Type-2 Fuzzy Interactive Search Algorithm. Performance of the proposed method is tested on different unconstrained mathematical functions and constrained structural and mechanical optimization problems. The attained results are compared with standard ISA method and seven other metaheuristic techniques through a suite of numerical and statistical evaluations. Drawing from the obtained results, the integration of the type-2 fuzzy decision module significantly enhances the algorithm's search capability. This improvement is evident in terms of stability, accuracy, and computational cost. It is worth noting that the employed comparative performance index for the proposed method indicates improvements of 3.38, 13.09, 16.61, and 27.23 percent compared to the best solution found by the selected methods for engineering problems.

Interval Type-2 Fuzzy Logic Controller for Speed Control of DC Motor in an Intelligent Manner

The method of variable speed control use in direct current motors adopting a fuzzy logic controller is fast replacing as compare some of the earlier conventional methods. In Electrical drive system a dc motor is fed from a fully controlled single phase convertor but the control is fails in the uncertain condition. This type of problem makes a critical issue in power plant where lots of motor are working. So, there is need to control dc motor in such uncertain condition. This work gives a design based on simulation of intelligent control by Interval Type-2 Fuzzy Logic Controller (IT2FLC). The designed fuzzy controller is effective in control performance in abnormal or uncertain conditions. The simulation study has been carried out and result are compared with type-1 FLC. The Result clearly demonstrate the superiority of the IT2FLC and compare to other methods proposed system is capable to control dc motors and performed better in both steady-state and cyclic conditions with more reliability

CONTRÔLE DIRECT DU COUPLE AVEC MODULATION VECTORIELLE SPATIALE UTILISANT DES RÉGULATEURS À LOGIQUE FLUUSE DE TYPE 2 À INTERVALLE D'UNE MACHINE À INDUCTION À DOUBLE ÉTOILE ALIMENTÉE PAR DEUX ONDULEURS À POINT NEUTRE À TROIS NIVEAUX SERRÉS

This paper introduces a direct torque control (DTC) method with space vector modulation (SVM) using interval type-2 fuzzy logic regulators for a dual-star induction machine fed by two three-level neutral point clamped inverters. The conventional DTC drive utilizes a pair of hysteresis comparators, which can lead to issues such as high torque ripple and variable switching frequency. As a result, we introduce several alternative methods, including DTC-SVM. We replace the hysteresis comparators with two Proportional-Integral (PI) regulators to enhance DTC performance, which generates the direct and quadrature voltage components in the d-q frame. The intricacy of the control system can make adjusting the PI regulator parameters difficult. To address this challenge, we propose using an interval type-2 fuzzy logic regulator for PI parameter adjustment in this paper. Our simulation results demonstrate the robustness and efficiency of the IT2FLC regulator.

First-order rule partitions-based decomposition technique of type-1 and interval type-2 rule-based fuzzy systems for computational and memory efficiency

Rule-based fuzzy systems are widely adopted in various fields for their effectiveness in reducing uncertainties. However, large rule bases present significant computational and memory challenges, especially for real-time applications. To overcome this limitation, this paper introduces a novel first-order rule partitions-based decomposition technique (FORPs-DT) for type-1 (T1) and interval type-2 (IT2) fuzzy logic systems (FLSs). This method involves decomposing the universe of discourse, membership functions (MFs), and fuzzy sets (FSs) by defining conditions for non-zero firing levels (intervals). Significantly, this approach allows the construction of subfuzzy systems with separate knowledge bases while maintaining the input–output relationship. The paper provides detailed explanations and visual representations of FORPs-DT and proposes a case of identical FORPs to illustrate its simplicity and resource optimization benefits. Extensive experiments demonstrate the superiority of this technique, showcasing not only reduced execution time and memory usage but also enhanced performance through identical partitions, which allows an increase in the number of FSs without additional computational or memory overhead.