Fuzzy Logic Unit Research Articles

Fuzzy Logic Enhanced Second-Order Sliding Mode Controller Design for an Experimental Twin Rotor System Under External Disturbances

Abstract Background The twin rotor model is frequently studied by researchers because although it has a basic structure the coupled pitch and yaw motions are adequately represented. However, it is quite difficult to obtain an efficient controller due to external disturbances. Classical sliding mode controller (SMC), which is of first order, is recognized to be robust in case of parameter changes and external disturbances especially when the sliding motion takes place, but it possesses chattering in the control input which may damage the mechanical parts of the system. Purpose In this study it was aimed to design a robust controller without chattering effect which will be used for the control of the twin rotor system in real time experiments. Methods To remedy the chattering issue, a novel fuzzy logic enhanced second-order sliding mode controller (FSOSMC) based on super twisting algorithm is proposed. This controller suppresses chattering while enhancing the robustness of the controller where the sliding surface slope parameter is updated online via a fuzzy logic unit. Then the proposed controller is implemented on an experimental twin-rotor system which has highly nonlinear and coupled dynamics. Results Real time experiments were performed on the twin rotor system using the proposed FSOSMC. For comparison purpose the SMC and second-order sliding mode controller (SOSMC) were also applied to the same system. The results have shown that the proposed controller increased the tracking performance without increasing the control effort while reducing the chattering. Conclusions The experimental results verified the success of the designed FSOSMC, therefore it may be recommended for the robust and precise control of aerial vehicles. Graphical Abstract

Construction of Universal Approximators for Multi-Input Single-Output Hierarchical Fuzzy Systems

As an important branch of fuzzy systems, hierarchical fuzzy system (HFS) has a wide range of applications in system science, medical science and engineering. Using the semi-tensor product of matrices, this paper investigates the universal approximation of multi-input single-output HFSs, and develops the construction algorithms of universal approximators. Initially, based on the fuzzy relation matrices of fuzzy logic units with Mamdani-type fuzzy rules, the algebraic formulation of HFSs consisting of fuzzy logic units is proposed. After that, the universal approximation of HFSs is explored via the algebraic formulation, and some effective algorithms are presented to construct the universal approximators of HFSs with respect to different scenarios of approximated functions. Finally, the effectiveness of obtained results is verified by the on-ramp metering of freeway.

A Review of Fuzzy Logic Method Development in Hydraulic and Pneumatic Systems

Fuzzy logic has been developed since the 1960s. Research related to fuzzy logic application in hydraulics and pneumatics is mainly aimed at energy demand reduction and improvement in operational characteristics. This article summarizes the recent achievements in hydraulic and pneumatic fuzzy logic system design. First, the main application areas have been identified, including control and fault diagnosis. The control systems were additionally grouped according to the main objects of study, such as pumps, actuators, proportional valves, etc. Then, the results of the recent research were presented, and the main features of the designed fuzzy logic units were summarized for each group. Particular attention was paid to types of membership functions used for fuzzification and defuzzification, numbers of fuzzy sets defined for input and output signals, types of fuzzy operators, the applied inference algorithm and the defuzzification method. Based on the analysis of the listed parameters, conclusions were formulated regarding advantages, main issues and difficulties, as well as recommended directions for further development.

FCUnet: Refined remote sensing image segmentation method based on a fuzzy deep learning conditional random field network

AbstractImage segmentation is pivotal for the understanding of high‐resolution remote sensing images (HRRS). However, because of the inherent uncertainties in remote sensing images and the highly complex resolution of HRRS, ambiguity often occurs among some geographic entities in the segmentation process, and the fine segmentation of HRRS is not considered sufficiently for most existing segmentation methods. Therefore, in this paper, the authors propose a new collaborative neural network structure called fuzzy deep learning conditional random field network (FCUnet) to solve the refined segmentation of HRRS. First, the authors design a fuzzy U‐Net classification network to obtain effective feature information, which introduces the fuzzy logic unit into the network to process the ambiguity and uncertainty of HRRS. Then, the authors introduce the conditional random field (CRF) at the end of FCUnet to optimize the image segmentation results. Finally, the authors validated the effectiveness and superiority of their approach on three data sets. The experiment results revealed that FCUnet had better refined segmentation performance and generalization ability than state‐of‐the‐art methods.

Algebraic Formulation and Application of Multi-Input Single-Output Hierarchical Fuzzy Systems With Correction Factors

Hierarchical fuzzy systems (HFSs) are successfully applied to control systems and computer networks. Particularly, correction factor is introduced to regulate fuzzy rules and alleviate the problems of memory usage and real-time performance of systems. This paper considers the multi-input single-output HFSs with correction factors, and proposes an algebraic framework by using the semi-tensor product of matrices. Firstly, a kind of fuzzy correction factor matrix (FCFM) is constructed to realize the fuzzification of correction factor, based on which, an algebraic form is developed for two-input single-output fuzzy systems. Secondly, the structure diagram of HFSs is decomposed into several fuzzy logic units, which have the form of two-input singleoutput fuzzy systems with FCFM. Based on this operation, the algebraic formulation of serial, parallel and hybrid HFSs with correction factors is proposed. Finally, the effectiveness of HFSs with FCFM is verified by studying the on-ramp metering of freeway.

Fuzzy Gain-Scheduling Based Fault Tolerant Visual Servo Control of Quadrotors

When military and civil missions such as transportation increase, fault tolerant control of unmanned aerial vehicles will be an obligation. Although onboard sensors provide information about the status of a quadrotor, the camera is not included in the list. In this study, visual servo control of quadrotors as a popular method for motion control is addressed. we address a visual servo control system for quadrotors as a popular method for motion control. The feature motions in the image plane are analyzed to reveal the relation between the actuator faults and these motions. Four AI fault approximators, a neural network, an extreme learning machine, a linear support vector machine, and a long short-term memory are used to approximate actuator faults of a quadrotor while using feature inputs. The results are convincing and the approximation results are used by a fuzzy logic unit to provide gain-scheduling based fault tolerant control. The proposed system shows sufficient results as a visual servo system for fixed and moving feature targets while providing fault tolerance.

FUZZY LOGIC CONTROL TO PROCESS CHANGE IRRADIATION AND TEMPERATURE IN THE SOLAR CELL BY CONTROLLING FOR MAXIMUM POWER POINT

This paper presents intelligent control methods to get the maximum power point (MPPT) to be a photovoltaic system that operates with high efficiency when weather conditions change as well as fluctuations in temperatures resulting from sunlight. The proposed method of controlling by fuzzy control techniques is applied with a Direct current to Direct current (DC-DC) converter device. The important steps of the control unit for integrated design. The photovoltaic system, which was designed by Matlab / Simulink, was implemented with simulations of autonomous water pumping techniques. Comparison of results with simulations without MPPT control. We have noticed that the system in the case of using the MPPT that was used in the fuzzy logic unit gave high efficiency for the energy production from the solar cell the crucial control unit steps for integrated design. Simulated autonomous water pumping methods were used to implement the Matlab/Simulink-designed photovoltaic system. Results comparison with simulations lacking MPPT control. We have observed that the system provided great efficiency for the energy generation from the solar cell in the event of using the MPPT that was used in the fuzzy logic unit.

Analytical Studies of the Application Aspects of Fuzzy Intuitionistic and Fuzzy Type 2 Set

The main aim of this paper is to introduce the definition of intuitionistic fuzzy set and operations on it that will prove fruitful to conclude to a solution on problems of real life and how fuzzy logic and Graphics Processing unit can be used together to solve different domain problems for faster responses. Various comprehensive survey of Type-2 fuzzy set and the intuitionistic fuzzy set theories have been described here. IFS is nothing but a simple extension of the fuzzy set defined in the domain of discourse. Graphics Processing Unit (GPU) has been used to enhance the speed of execution. The intuitionistic fuzzy set is utilized as a proper element for representing the degree of hesitation concerning both the non-membership and membership degrees of an element of a set. Recent trends show how GPU is used for various computational applications that are run parallel to reduce overall execution time. Both the fuzzy domains seek application in tackling of the situation of vagueness with their respective characteristic feature sets to the fields concerned to different engineering domains of interdisciplines. This paper highlights the overview of the two eminent fuzzy categories like Fuzzy type 2 and intuitionistic fuzzy theory along with their application on greater scale.

Parallel fuzzy relation matrix factorization towards algebraic formulation, universal approximation and interpretability of MIMO hierarchical fuzzy systems

Multi-input multi-output (MIMO) hierarchical fuzzy systems (HFSs) play a significant role in industrial process control and power systems. Using the max-min semi-tensor product of matrices, this paper proposes the algebraic formulation of MIMO HFSs, and discusses the corresponding interpretability. Firstly, the parallel fuzzy relation matrix (FRM) of MIMO fuzzy systems is proposed, based on which, an algebraic formulation of MIMO fuzzy systems is developed. Secondly, the factorization of parallel FRM is used to partition the MIMO fuzzy systems into fuzzy logic units (FLUs). Based on this operation, the algebraic formulations and construction algorithms of serial, parallel and hybrid MIMO HFSs are established. Thirdly, the algebraic formulations are used to study the universal approximation and interpretability of MIMO HFSs, and the optimal interpretability is obtained. Finally, the effectiveness of the obtained results is verified by the energy control systems in parallel hybrid vehicles.

The onboard expert measuring system for assessing the technogenic risk during towing operations based on the fuzzy logic

The article describes a method used to create an on-board measuring system to determine the degree of risk in towing operations using the mathematical apparatus of fuzzy logic. Based on the method of paired comparisons and transformations and using the interpolation method, membership functions of linguistic variables for determining the degree of risk were calculated. To eliminate the misperception of control commands in the tug in towing operations, the concordance component of risks was used. This method was used to assess the risk value for mooring with several operations. The on-board measuring system for risk assessment was developed. Its main component is a data bank containing factors that affect an assessment of the state and results of the fuzzy logic unit. The system can assist skippers in the decision-making process in shipboard operations. The fuzzy logic system was tested by interviewing maritime workers; the result proved the adequacy of this system.

Remote sensing image segmentation based on the fuzzy deep convolutional neural network

ABSTRACT Remote sensing image segmentation has large uncertainty related to the heterogeneity of similar objects and complex spectrum in satellite images, causing the traditional segmentation methods to be greatly limited. Existing semantic segmentation methods represented by deep learning have made breakthrough progress. However, traditional deep learning methods, such as deep convolution neural network, are a completely deterministic model, which cannot describe the uncertainty of remote sensing image well. To solve this problem, a new deep neural network combined with fuzzy logic units is proposed in this paper, called RSFCNN (Remote Sensing image segmentation with Fuzzy Convolutional Neural Network). The network integrates convolution units and fuzzy logic units. Convolution units are used to extract discriminant features with different proportions, thus providing comprehensive information for pixel-level remote sensing image segmentation. Fuzzy logic units are used to deal with various uncertainties and provide more reliable segmentation results, and each unit handles the feature maps at a particular image scale by Gaussian blur function. Experiments were carried out on two data sets, and the results showed that RSFCNN has higher segmentation accuracy and better performance than state-of-the-art algorithms.

Adaptive Motion Cueing Algorithm Based on Fuzzy Logic Using Online Dexterity and Direction Monitoring

Motion cueing algorithm (MCA) is used to reproduce the realistic driving motion feeling for the user of the virtual vehicle using linear acceleration and angular velocity motion signals while respecting the physical constrains of the simulation-based motion platform. Classical washout filter is the most common and commercial type of the MCA because of easy tuning, short processing time, and simplicity. The classical washout filter is a conservative method in using workspace because of the worst-case scenario tuning technique. Also, the existing adaptive washout filter based on time-varying cut-off frequency does not consider the dexterity of the motion platform as considering this factor leads to use the high acceleration areas of the platform workspace. In this article, an adaptive washout filter is designed and developed based on time-varying cut-off frequency using dexterity and direction monitoring factors to use the available high acceleration areas of the platform for better generation of the high-frequency part of the motion signal. The proposed adaptive washout filter is composed of three fuzzy logic units for each mode including longitudinal, lateral, yaw, and heave and each unit has two inputs including motion sensation error between the real vehicle driver and user of the motion platform and the weight of the end-effector which is a parameter based on dexterity and current position of the end-effector. Each fuzzy logic unit has also one output which is the selected cut-off frequency of the related washout filter. The proposed adaptive washout filter uses the high dexterous areas of the workspace because it considers the dexterity of the motion platform in calculation of the end-effector weight as the second input of the fuzzy logic units. The proposed adaptive washout filter is validated in MATLAB/Simulink programming language and the experimental results indicate that the proposed adaptive washout filter performed better than existing adaptive and classical washout filters in terms of motion sensation error between the driver of real vehicle and the user of motion platform as well as usage of the platform workspace.

Application of fuzzy-logic to design fuzzy compensation controller for speed control system to reduce vibration of CBШ-250T drilling machine in mining industry

The paper introduces fuzzy compensation control algorithm based on fuzzy logic to control the rotation speed in CБШ-250T drilling machine. The proposed solution uses an artificial neural network instead of a vibration measuring sensor to identify the amplitude and vibration frequency on a rotary drill. The vibration amplitude, vibration frequency and setpoint of the drilling speed are the input variables for the fuzzy logic unit. The fuzzy tool will diagnose the compensatory parameter δα with the target to reduce the vibration of the drilling equipment. The results were tested through modeling using Simulink_matlab tool. It can be applied to control system to improve control quality, and reduce vibration for CБШ-250T drilling machine, which is being operated on mines in Quang Ninh area.

Autonomous omnidirectional mobile robot navigation based on hierarchical fuzzy systems

PurposeThis paper aims to propose a one-layer Mamdani hierarchical fuzzy system (HFS) to navigate autonomously an omnidirectional mobile robot to a target with a desired angle in unstructured environment. To avoid collision with unknown obstacles, Mamdani limpid hierarchical fuzzy systems (LHFS) are developed based on infrared sensors information and providing the appropriate linear speed controls.Design/methodology/approachThe one-layer Mamdani HFS scheme consists of three fuzzy logic units corresponding to each degree of freedom of the holonomic mobile robot. This structure makes it possible to navigate with an optimized number of rules. Mamdani LHFS for obstacle avoidance consists of a number of fuzzy logic units of low dimension connected in a hierarchical structure. Hence, Mamdani LHFS has the advantage of optimizing the number of fuzzy rules compared to a standard fuzzy controller. Based on sensors information inputs of the Mamdani LHFS, appropriate linear speed controls are generated to avoid collision with static obstacles.FindingsSimulation results are performed with MATLAB software in interaction with the environment test tool “Robotino Sim.” Experiments have been done on an omnidirectional mobile robot “Robotino.” Simulation results show that the proposed approaches lead to satisfied performances in navigation between static obstacles to reach the target with a desired angle and have the advantage that the total number of fuzzy rules is greatly reduced. Experimental results prove the efficiency and the validity of the proposed approaches for the navigation problem and obstacle avoidance collisions.Originality/valueBy comparing simulation results of the proposed Mamdani HFS to another navigational controller, it was found that it provides better results in terms of path length in the same environment. Moreover, it has the advantage that the number of fuzzy rules is greatly reduced compared to a standard Mamdani fuzzy controller. The use of Mamdani LHFS in obstacle avoidance greatly reduces the number of involved fuzzy rules and overcomes the complexity of high dimensionality of the infrared sensors data information.

Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.

Fuzzy logic-based multi-level shunt active power filter for harmonic reduction

In this paper, using a three level diode clamped multilevel inverter and DC capacitor, a shunt active power filter (SAPF) is implemented to mitigate the supply current harmonics and compensate reactive power drawn from nonlinear load. The advantage of using three-level inverter paves way to reduced harmonic distortion and switching losses. Fuzzy logic control and unit sine vector control are proposed in this paper for generating reference current for the SAPF. The advantage of fuzzy control is that it is based on a linguistic description and does not require a mathematical model of the system. The implementation of fuzzy logic control (FLC) algorithm is executed using MATLAB fuzzy logic tool box. The proposed pulse width modulation (PWM) method produces the switching signals to the inverter from the sampled reference phase voltage magnitudes as in the case of conventional space vector PWM (SVPWM). The simulation results illustrate that the proposed three-level SAPF with low harmonic content in supply current and in phase with the line voltage. The simulation results are validated with prototype model for demonstrating the effectiveness of the system.

Fuzzy logic-based multi-level shunt active power filter for harmonic reduction

In this paper, using a three level diode clamped multilevel inverter and DC capacitor, a shunt active power filter (SAPF) is implemented to mitigate the supply current harmonics and compensate reactive power drawn from nonlinear load. The advantage of using three-level inverter paves way to reduced harmonic distortion and switching losses. Fuzzy logic control and unit sine vector control are proposed in this paper for generating reference current for the SAPF. The advantage of fuzzy control is that it is based on a linguistic description and does not require a mathematical model of the system. The implementation of fuzzy logic control (FLC) algorithm is executed using MATLAB fuzzy logic tool box. The proposed pulse width modulation (PWM) method produces the switching signals to the inverter from the sampled reference phase voltage magnitudes as in the case of conventional space vector PWM (SVPWM). The simulation results illustrate that the proposed three-level SAPF with low harmonic content in supply current and in phase with the line voltage. The simulation results are validated with prototype model for demonstrating the effectiveness of the system.

Heuristic design of fuzzy inference systems: A review of three decades of research

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi–Sugeno–Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper ofers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems.

INFORMATION TECHNOLOGIES FOR THE ANALYSIS OF THE STRUCTURAL CHANGES IN THE PROCESS OF IDIOPATHIC MACULAR RUPTURE DIAGNOSTICS

Process of eye tomogram obtaining by means of optical coherent tomography is studied.Stages of idiopathic macula holes formation in the process of eye grounds diagnostics are considered. Main stages of retina pathology progression are determined: Fuzzy logic units for obtaining reliable conclusions regarding the result of diagnosis are developed. By the results of theoretical and practical research system and technique of retinal macular region of the eye state analysis.

Fuzzy robust backstepping with estimation for the control of a robot manipulator

In this study, a new fuzzy robust backstepping controller with estimation is proposed for the control of a robot manipulator. Backstepping control is preferred because the Lyapunov function that is used in stability analysis and the feedback control law that is used for control purpose are defined systematically during controller design. Fuzzy logic units are designed to update the gains of the backstepping controller. Then, the proposed controller is applied to a robot manipulator that is to track a trajectory in a three-dimensional space while it is subjected to external disturbances and parameter variations. The numerical results demonstrate and verify the successful performance of the proposed controller.