Design Of Membership Functions Research Articles

Nonlinear Multivariable System Identification: A Novel Method Integrating T-S Identification and Multidimensional Membership Functions

In this paper, a new multidimensional Takagi–Sugeno (T-S) identification technique is proposed for multivariable nonlinear systems. In this technique, multidimensional membership functions are designed using concepts from solid mechanics. The design of membership functions is carried out in multidimensional space, defining the principal axes from the eigenvectors of the inertia matrix, and it has the characteristic of dividing the space into regions with the same inertia. These regions are analyzed to define the center of gravity for each rule. Illustrative examples of multivariable nonlinear systems, such as a thermal mixing process and a binary distillation column, are selected to evaluate the effectiveness of the proposed method. The proposed method is compared with traditional T-S identification that uses one-dimensional membership functions and shows a reduction in the relative identification error and the algorithm execution time. Additionally, the proposed method prevents rules from being positioned outside the system’s range, thereby avoiding the generation of unnecessary rules.

Design of self-organized membership functions for an optimal fuzzy controller in enhancing frequency regulation

Design of self-organized membership functions for an optimal fuzzy controller in enhancing frequency regulation

Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques

In this study, a method for optimizing membership function tuning for fuzzy control of robotic manipulators using PID-driven data techniques is presented. Traditional approaches for designing membership functions in fuzzy control systems often rely on the experience and knowledge of the system designer, which can lead to suboptimal performance. By utilizing data collected from a PID control system, the proposed method aims to enhance the precision and controllability of robotic manipulators through improved fuzzy logic control. A Mamdani-type fuzzy logic controller was developed and its performance was simulated in Simulink, demonstrating the effectiveness of the proposed optimization technique. The results indicate that the method can outperform conventional P control systems in terms of overshoot reduction while maintaining comparable transient response specifications. This research highlights the potential of the PID-driven data-based approach for optimizing membership function tuning in fuzzy control systems and offers valuable insights for the development and evaluation of fuzzy logic control in robotic manipulators. Future work may focus on further optimization of the tuning process, evaluation of system robustness under various operating conditions, and exploring the integration of other artificial intelligence techniques for improved control performance.

A Serious Game System for Upper Limb Motor Function Assessment of Hemiparetic Stroke Patients.

Stroke often results in hemiparesis, impairing the patient's motor abilities and leading to upper extremity motor deficits that require long-term training and assessment. However, existing methods for assessing patients' motor function rely on clinical scales that require experienced physicians to guide patients through target tasks during the assessment process. This process is not only time-consuming and labor-intensive, but the complex assessment process is also uncomfortable for patients and has significant limitations. For this reason, we propose a serious game that automatically assesses the degree of upper limb motor impairment in stroke patients. Specifically, we divide this serious game into a preparation stage and a competition stage. In each stage, we construct motor features based on clinical a priori knowledge to reflect the ability indicators of the patient's upper limbs. These features all correlated significantly with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), which assesses motor impairment in stroke patients. In addition, we design membership functions and fuzzy rules for motor features in combination with the opinions of rehabilitation therapists to construct a hierarchical fuzzy inference system to assess the motor function of upper limbs in stroke patients. In this study, we recruited a total of 24 patients with varying degrees of stroke and 8 healthy controls to participate in the Serious Game System test. The results show that our Serious Game System was able to effectively differentiate between controls, severe, moderate, and mild hemiparesis with an average accuracy of 93.5%.

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.

Using fuzzy inference to plan capital repairs and reconstruction of public buildings

Introduction. The article analyzes the determinants that condition the incorporation of public buildings into capital repair and reconstruction plans (CRR plan). These include the technical condition of a facility, specified in an expert opinion and identified on the basis of the survey results, the duration of effective operation, determined in accordance with guideline values, the work performance time, the availability of the engineering and transport infrastructure, and other factors.
 Materials and methods. The co-authors formulated fuzzy inference rules and compiled a table of expert opinions issued on their basis. To implement the fuzzy inference procedure, the co-authors applied the Mamdani algorithm, the min-conjunction for aggregating indicators, the max-disjunction for accumulating conclusions, and the centre of gravity method to ensure defuzzification. The Editor of Fuzzy Output Systems of the MatLab package is used to implement the proposed fuzzy inference pattern. The Saati hierarchy method is used to design membership functions (FP). The analysis of literature sources did not identify any works in which fuzzy logic methods or fuzzy inference rules were used to plan the CRR of public buildings.
 Results. Membership functions for all factors and the final indicator on the selected scale, corresponding to the Harrington desirability function in the range of values [0; 100], are designed. The results of the implementation of the proposed fuzzy inference system in the MatLab environment are presented in the form of graphs and numerical values of all input linguistic variables.
 Conclusions. Fuzzy inference allows to obtain the numerical value of the integral repair potential that underlies an informed decision about the incorporation of a facility into the CRR plan. The strength of the approach is the modifiability and expandability of the rule base in practical work. The proposed planning tool allows to consider a combination of principal factors, cut costs, reduce the time frame and improve the public building repair quality.

Power Optimal Control Method of Network Big Data for Mechanical Vibration Wireless Sensor

With the gradual deepening of research on wireless sensor networks at home and abroad in recent years, some wireless sensor network application systems have emerged in the fields of academic research and engineering applications. Especially in the industrial field, wireless sensor network, as a new information perception technology, has attracted more and more attention due to its advantages of self-organization, flexible deployment, strong scalability, and on-chip processing capabilities. The purpose of this paper is to study the power optimization control method of wireless sensor of mechanical vibration by network big data. In view of the current problem of power control of mechanical vibration wireless sensor network nodes, first, under the premise of ensuring the accuracy of data collection, the key factors affecting the power control of mechanical vibration wireless sensor network nodes are analyzed, the wireless sensor power control model is designed, and the network big data is proposed. Impact on the power of wireless sensors; finally, based on the above research, the fuzzy power control algorithm and its simulation are specifically studied. First, a point-to-point transmission power control system model is designed, and the fuzzy logic controller module is designed in detail, including fuzzy Part of the membership function design, fuzzy rule determination and the use of defuzzification methods. Then use simulation tools to build a point-to-point simulation model and a simulated network simulation model. Finally, these two models are used to compare the performance of fuzzy power control algorithm and fixed power method. The experimental results show that the network survival time using fuzzy power control algorithm is twice that of the fixed power method. The fuzzy power control algorithm has great advantages in both effective communication distance and network survival time.

Multilayer Interval Type-2 Fuzzy Controller Design for Hyperchaotic Synchronization

This study presents a multilayer interval type-2 fuzzy controller, which applies to synchronize the hyperchaotic systems. The main contribution of this study is the design of a multilayer structure in the membership function space, which can improve the learning ability and flexibility of the type-2 fuzzy network. Using the proposed multilayer structure, the fuzzy network can reduce the number of rules while ensuring synchronization performance. Particularly, the design of 3-dimensional Gaussian membership functions (3-DGMFs) enhances the ability to against system uncertainties and external disturbances. Moreover, the structure of the proposed network can be autonomously constructed by using the self-evolving algorithm. The parameters of the proposed network are online updated using an adaptive law obtained by the gradient descent method. Finally, numerical simulations on the synchronization of hyperchaotic systems are conducted to validate the efficiency and performance of the proposed method.

Chaotic multi verse optimizer based fuzzy logic controller for frequency control of microgrids

This paper represents an arrangement of fuzzy PD and derivative filter based PID controller (FPIDF) with its optimally designed membership functions and is employed for the load frequency control (LFC) of isolated multi- microgrids. The performance of a recently developed multi verse optimizer (MVO) algorithm is improved by introducing chaotic map appropriately in the algorithm. The improved algorithm, named as CMVO algorithm is applied to tune the proportional integral derivative (PID) controller gains for the LFC of microgrid. The superiority of the proposed CMVO algorithm is examined by its superior performance compared to MVO PID, recently published IAYA PID, JAYA PID and GA PID in the microgrid system. Further, CMVO based FPIDF with its optimized membership position (FPIDF-OM) outperforms compared to CMVO based FPIDF, IJAYA tuned FPID and FPD/PI-PD controller in microgrid system. Better dynamic performances are found with the proposed technique for stochastic type wind power, solar irradiance and load variations in the microgrid compared with FPIDF and PID. The robustness of the proposed technique is established by the sensitivity analysis. The proposed design approach has been extended to a microgrid model including biogas turbine generator, biodiesel engine generator and aqua electrolyser along with fuel cell unit. Finally, OPAL-RT based hardware-in-the-loop simulation of proposed techniques has been done.

Design of Interval Type-2 Fuzzy Controllers for Active Magnetic Bearing Systems

This article proposes an active levitation control system composed of an active magnetic bearing (AMB) rotor and an interval type-2 (IT2) model-based fuzzy logic controller (FLC). The controller aims to achieve fast and stable levitation by compensating for system uncertainties via proper design of IT2 membership functions. In particular, the complex nonlinear dynamics of the AMB are modeled as a set of local linear systems around multiple operating points, which can be stabilized by a parallel distributed compensation scheme. Sufficient conditions are derived to guarantee the asymptotical stability of the closed-loop system based on the linear matrix inequality approach and the Lyapunov stability theory. Moreover, input constraints are applied in an enhanced version of the controller. Experiments on a real AMB platform were conducted to demonstrate the effectiveness and superiority of the proposed IT2 FLC.

Cluster-Based Membership Function Acquisition Approaches for Mining Fuzzy Temporal Association Rules

In real-world applications, transactions are typically represented by quantitative data. Thus, fuzzy association rule mining algorithms have been proposed to handle these quantitative transactions. In addition, items generally have certain lifespans or temporal periods in which they exist in a database. Therefore, fuzzy temporal association rule mining algorithms have also been proposed in the literature. A key factor in the acquisition of fuzzy temporal association rules (FTARs) is the design of appropriate membership functions. Because current approaches have been designed to generate membership functions for mining fuzzy association rules (FARs) in market-basket analysis, in this paper, we propose a membership function tuning mechanism for a fuzzy temporal association rule mining algorithm. The proposed approach modifies an existing cluster-based method to generate unique membership functions that are specifically tailored to each item in a dataset. Two factors are utilized to decide the appropriate membership functions of each item: (1) the density similarity among intervals corresponding to the density similarity within intervals, and (2) the information closeness within an interval corresponding to the similarity in the number of data points between intervals. A parameter θ is used to indicate the relative importance of these two factors. As a result, the membership functions are generated based on the quantitative ranges of individual items, and the generated membership functions of items are different in terms of the values of each interval and the number of intervals. The generated membership functions are subsequently used in a fuzzy temporal association rule mining algorithm. Computational experiments were conducted on both a synthetic dataset and a real-world one to demonstrate the effectiveness of the proposed approach.

Bounded Interval Fuzzy Control for Half Vehicle’s Active Suspension System

A novel efficient control scheme for an active vehicle suspension system is to be designed and simulated in this paper. A half car model has been designed and controlled using two different schemes of standard fuzzy control and bounded interval fuzzy control using MATLAB/SIMULINK. The bounded interval fuzzy control is designed to reduce the uncertainties in the fuzzy sets system and solve the non-linear control problem that the standard fuzzy control cannot handle it. It should be noted that fuzzy logic system is capable of dealing with imprecise concepts and numerous vague but the design of membership functions is nontrivial task. This is because of uncertainty degree that is caused due to road inputs profiles, fuzzy knowledge rules and immeasurable disturbance. The proposed method is expected to be able to mimic the heuristic knowledge of design the membership functions which depends on degree of uncertainty. The membership functions will be generated online during the process in order to deal with uncertainties. The simulation results have demonstrated that the proposed control exhibits better performance and stability as compared to standard fuzzy logic. In addition, the proposed scheme presents a preferable solution and balancing achievement between ride comfort and handling performance. These results demonstrated that the body accelerations and tire dynamic loads will be reduced for the vehicle suspension system in either automobiles or robotics suspension systems.

Improved scheme of membership function optimisation for fuzzy air‐fuel ratio control of GDI engines

This study researches an improved scheme of membership function optimisation (MFO) for fuzzy air–fuel ratio (AFR) control of gasoline direct injection (GDI) engines based on correspondence analysis (CA). This proportional–integral-like fuzzy knowledge-based controller (FKBC) optimised by the proposed scheme can further optimise AFR control performance while maximising conversion efficiency of the three-way catalyst to eliminate the exhaust emissions in real time. Different from the conventional experience-based membership function (MF) design method for an FKBC, the proposed MFO scheme uses CA approach and can visualise the relationship between engine step gain scenarios and designed MF patterns to precisely determine its scalar parameters for AFR regulation of GDI engines. Within this context: (i) specialised MFs for self-adaptive AFR control system of a GDI engine are designed with weight distribution; (ii) based on designed scalar parameters, the CA model with taxonomic dimensions is built for acquiring a customised MF to counter transient scenario changes more effectively; (iii) the engine controller with the proposed scheme is real time validated in a production V6 GDI engine, and its advantage in terms of engine transient control performance is further demonstrated by comparing with a benchmark controller designed based on experience.

Energy Management using Optimal Fuzzy Logic Control in Wireless Sensor Network

Energy is a key factor that affects the lifetime of wireless sensor network (WSN). This paper proposes an adaptive energy management model to improve the energy efficiency in WSN. Unlike existing clustering routing protocols, the overall performance indicators are introduced as the inputs of fuzzy logic control (FLC). Meanwhile, the probability adjustment value, as the out of FLC, is fed back to the network for the generation of new clusters. Since the design of membership functions (MFs) of FLC has a significant impact on system performance, a particle swarm optimization (PSO) algorithm is used to optimize MFs and its optimization goal is to reduce the number of dead nodes and increase the remaining energy level in WSN. Simulation experiments were conducted for the low energy adaptive clustering hierarchy protocol (LEACH), the conventional FLC, FLC using genetic algorithm (GA), and FLC using PSO. The results show that the proposed FLC-PSO has the best performance among the four protocols and it can be used efficiently in energy management of WSN.

Systematic design of membership functions for fuzzy logic control of variable speed refrigeration system

The systematic design of membership functions (MFs) for the fuzzy logic control (FLC) of a variable speed refrigeration system (VSRS) is presented in this paper to reduce trial and error as well as dependence on expert knowledge. In particular, the influence of MFs on the error and error change rate of controlled variables is investigated in detail to verify the validity of the designed MFs. To this end, the range of MFs obtained based on the control accuracy and experimental data for the static and dynamic states of the VSRS were used as the criterion values. Two different values from the criterion were then used to compare the control performance through simulations and experiments. Some simulations and real experiments for the VSRS were conducted to verify the validity of the designed MFs. The experimental results showed good agreement with the simulation results. The error change rate and sampling time strongly affected the control performance of the VSRS in the transient state. Moreover, the desired control accuracy at steady-state, an essential element in the design specifications, was successfully achieved using the proposed approach. The suggested method can be applied using the control accuracy and simple experimental results from the static and dynamic states of the target system without depending on iterative work when designing MFs, which will be helpful for beginners.

Application of a Genetic-Fuzzy FMEA to Rainfed Lowland Rice Production in Sarawak: Environmental, Health, and Safety Perspectives

Rainfed lowland rice is the most popular choice for rice cultivation in Sarawak, Borneo. In general, rice production in Sarawak consists of seven phases, namely, preparing land, establishing crop, transplanting, managing crop, harvesting, post-harvesting, and milling. Most farmers in Sarawak depend on indigenous knowledge and experience for rice cultivation. In this paper, an improved fuzzy failure mode and effect analysis (FMEA) with genetic algorithm-based design of fuzzy membership functions and monotone fuzzy rules relabeling is employed as a knowledge-based tool for risk analysis and assessment pertaining to rice production in Sarawak. The specific focus is on issues related to the environment as well as health and safety of farmers and consumers. With the support from the Sarawak Government, we analyze useful data and information pertaining to various rice fields from experienced farmers to develop the fuzzy FMEA model. Specifically, we develop fuzzy FMEA to inculcate the best practices for farmers to improve yield and enhance food safety. Through this paper, we identify that musculoskeletal disorders due to bad postures is the most noticeable occupational health hazard; as a result, new techniques and tools are invented and introduced to mitigate this risk. In summary, this is a new attempt to implement a quality and risk assessment tool that contributes toward enhancing rice productivity in Sarawak, and modernizing the local agricultural sector.

Oxidized hyaluronic acid modified by l-Argine for siRNA delivery

Design of reasonable membership functions (MFs) is a primary problem for the fuzzy modeling method. Considering the complex nonlinear characteristics of blast furnace gas (BFG) system in steel industry, a MFs learning method based on clustering analysis is proposed in this paper, where a multi-objective density clustering method is reported by combing the targets of the model accuracy, complexity and interpretability. In order to simplify the modeling process and fit the distribution characteristics of industrial data, a simple type of function is designed and the optimized clustering results are used for determining the parameters of fuzzy MFs. To verify the performance of the proposed method, the practical data coming from a steel plant are employed. The experiment results demonstrate that the MFs designed by the proposed method could effectively improve the accuracy, complexity and interpretability of the fuzzy model, which provide helpful information for the fuzzy modeling of BFG pipeline pressure.

A multi-objective clustering-based membership functions formation method for fuzzy modeling of gas pipeline pressure

Design of reasonable membership functions (MFs) is a primary problem for the fuzzy modeling method. Considering the complex nonlinear characteristics of blast furnace gas (BFG) system in steel industry, a MFs learning method based on clustering analysis is proposed in this paper, where a multi-objective density clustering method is reported by combing the targets of the model accuracy, complexity and interpretability. In order to simplify the modeling process and fit the distribution characteristics of industrial data, a simple type of function is designed and the optimized clustering results are used for determining the parameters of fuzzy MFs. To verify the performance of the proposed method, the practical data coming from a steel plant are employed. The experiment results demonstrate that the MFs designed by the proposed method could effectively improve the accuracy, complexity and interpretability of the fuzzy model, which provide helpful information for the fuzzy modeling of BFG pipeline pressure.

An Adaptive Complementary Kalman Filter Using Fuzzy Logic for a Hybrid Head Tracker System

In this paper, a sensor fusion algorithm that integrates gyroscope and vision measurements using an adaptive complementary Kalman filter is proposed to estimate the attitude of a hybrid head tracker system. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, an adaptive fading filter is implemented to the sensor fusion filter, and fuzzy logic is applied to adjust the fading factor, which adapts a Kalman gain of the sensor fusion filter. For recognizing the dynamic condition of the system and vision measurement fault, the normalized square error of attitude and the norm of gyroscope output with designed membership functions are used. The performance of the proposed algorithm is evaluated by simulations. It is confirmed that the proposed algorithm has better performance than the conventional algorithms in high-dynamic conditions and vision measurement fault case.

Mean-Based Fuzzy Control for a Class of MIMO Robotic Systems

This paper presents mean-based fuzzy controllers for trajectory tracking for a class of multiple-input multiple-output robotic systems with nonaffine-like form and parametric uncertainties, in which direct adaptive controllers with state estimators are developed via a mean-based fuzzy identifier without prior knowledge of the membership functions. By using the proposed adaptive technique, unfavorable influence from the initial design of membership functions can be effectively diminished. Moreover, the computation burden of the adaptive laws can be successfully alleviated because the derivative of the fuzzy systems is not required. A Lyapunov-based stability analysis is utilized to guarantee successful system control and desired tracking performance of the closed-loop system. Finally, two examples are provided to demonstrate the feasibility of the proposed control method.