Fuzzy Control Research Articles

Fuzzy expert system for ergonomic assembly line worker assignment and balancing problem under uncertainty

ABSTRACT In the era of Industry 5.0, there is a significant gap in addressing ergonomic risks and imprecise task times in manufacturing systems. This study aims to fill this gap by extending the ergonomic assembly line balancing problem with worker assignment. It employs a novel two-phase framework combining a constructive heuristic for feasibility with a unique ergonomic assessment method developed through a fuzzy expert system. Validated using 96 synthesized numerical instances, the proposed method addresses the scarcity of fuzzy and ergonomic-oriented data in benchmarks. Then, computational results are thoroughly analyzed to evaluate the method’s performance and identify potential areas for further research. The proposed optimization method provided high-quality solutions with a majority demonstrating low ergonomic risk and high worker safety, contributing to an overall improvement in ergonomic conditions. The integration of fuzzy expert system and advanced optimization techniques yields a robust framework for achieving a safer and more efficient manufacturing environment.

Aggregators Used in Fuzzy Control—A Review

Aggregators Used in Fuzzy Control—A Review

Antisaturation fixed-time backstepping fuzzy integral sliding mode control for automatic landing of fixed-wing unmanned aerial vehicles

This paper proposes a robust adaptive fixed-time control for automatic landing systems (ALS) of small-scale fixed-wing UAVs exposed to wind disturbances, parametric uncertainties, and input saturation. The control structure consists of three subsystems, i.e., the guidance system, the attitude control system, and the thrust control system. Considering the landing geometry, the guidance system computes the desired Euler angles to stabilize the dynamics of altitude deviation, lateral deviation, and lateral speed. An auxiliary system is designed to compensate for the effect of input saturation in the attitude control system. The fixed-time method, the backstepping control (BSC), the fuzzy logic control (FLC), and the integral sliding mode control (ISMC) techniques are adopted to synthesize the control law. All three functional parts of the proposed ALS are equipped with a fixed-time fuzzy sliding mode disturbance observer (FSMDO). FLC computes the gains of signum functions adaptively to attenuate the chattering. Fixed-time convergence of the system errors to an arbitrary residual set is proved by comprehensive stability analysis. Finally, the comparative simulation results verify the better dynamic response and stronger robustness under the proposed ALS.

The fuzzy inference system based on axiomatic fuzzy sets using overlap functions as aggregation operators and its approximation properties

The fuzzy inference system based on axiomatic fuzzy sets using overlap functions as aggregation operators and its approximation properties

Stability and Stabilization of Delayed Fuzzy Semi-Markov Jump Systems with Incomplete Transition Rates and Quadratic Fuzzy Lyapunov Matrix via Quantized Control Design

Stability and Stabilization of Delayed Fuzzy Semi-Markov Jump Systems with Incomplete Transition Rates and Quadratic Fuzzy Lyapunov Matrix via Quantized Control Design

Optimal robust adaptive fuzzy controllers based on sliding surfaces for inverted pendulum systems

Optimal robust adaptive fuzzy controllers based on sliding surfaces for inverted pendulum systems

Improvement of stability on dc microgrid using dual series virtual impedance based fuzzy controller under variation of constant power load

Improvement of stability on dc microgrid using dual series virtual impedance based fuzzy controller under variation of constant power load

Identification of generator coherency in power systems with wind farm

In the context of the "dual carbon" goal, the penetration rate of new energy represented by wind power is gradually increasing. The large-scale grid connection of wind power makes the power system more complex and uncertain. The output of wind farms changes the system flow, indirectly affecting the power angle characteristics of synchronous generators, and thereby changing the coherence between generators. Affects synchronous generator homology identification. This paper proposes a generator homology identification method for power systems containing wind farms, in response to the problem that the existing methods for identifying unit homology have not taken into account the adverse effects of wind farms on identification results. Translate the influence of wind farms on the homology of synchronous generators into the contraction admittance matrix as a static electrical distance indicator; Select dynamic data reflecting the homology of synchronous generators after disturbance, and form four dynamic indicators to measure the power angle increment curve between each generator: Euclidean distance, Chebyshev distance, grey correlation, and correlation coefficient; By using the combination weighting method to determine the weights of each indicator, a comprehensive similarity matrix is formed, and the optimal clustering results are determined using fuzzy system clustering and F-statistical values. Finally, the effectiveness of the proposed method was verified using EPRI-9 node system, EPRI-36 node system, and IEEE68 node system as examples.© 2017 Elsevier Inc. All rights reserved.

Self-triggered fuzzy trajectory tracking control for the stratospheric airship

A self-triggered trajectory tracking control algorithm, which considers unknown disturbance and actuator saturation, has been studied to address the energy and onboard equipment life limitation problems of stratospheric airships. The controller is based on a backstepping-sliding mode method, utilizing the fuzzy logic systems and auxiliary systems to handle unknown disturbance and actuator saturation, respectively. Based on the continuity of the control law, the self-triggered condition is designed whose paradigm is used to build the fuzzy logic systems. Compared with the existing stratospheric airships event-triggered controller, continuous state detection and calculation are avoided. It is proved that the tracking errors and the weight matrix errors of the fuzzy logic system are uniformly ultimately bounded both at the trigger moment and within the trigger interval. Additionally, Zeno behavior is avoided without imposing additional parameter restrictions. Simulation results demonstrate the effectiveness of the proposed algorithm. Based on execution frequency, the proposed algorithm can conserve 98.3% of resources compared to the traditional algorithm and 97.5% of resources compared to the event-triggered algorithm.

Optimizing cloud service provider selection with firefly-guided fuzzy decision support system for smart cities

Businesses that want to benefit from cloud computing must choose a Cloud Service Provider (CSP). Cost, performance, Reliability, security, and SLAs must be evaluated during the decision process. CSP assessment is tough because of uncertainties and erroneous data. Fuzzy logic and the firefly optimization technique have been proposed in this paper to achieve optimal results based on diverse components. The proposed methodology uses consumer, service provider, and public reviews based on the three elements. These components' ratings can be used to analyze efficiency. Simple fuzzy logic is inferior to optimized fuzzy logic, according to experiments. The Firefly Optimized Fuzzy DSS is compared against non-optimized fuzzy decision-making systems and standard optimization methods. The results show that the proposed model is better for selecting the best CSP based on many parameters and managing assessment uncertainty. Fuzzy logic and optimization methods provide more nuanced and precise decision-making that accounts for subjective assessments and confusing facts. Businesses can make informed choices and ensure their CSP needs are satisfied with the approach. Finally, the Firefly Optimized Fuzzy Decision Support System offers a new perspective on cloud service provider selection by merging fuzzy logic with optimization. The system's ability to handle poor evaluations and ambiguity makes it ideal for CSP selection's complex decision-making process. This paper helps build decision support systems for choosing a cloud service provider and has substantial implications for firms seeking successful cloud computing solutions. This research work's conclusions have major implications for corporations and organizations searching for the finest cloud service providers. CSP-related real-world datasets are tested experimentally.

Research On Water Level Control of The Great Lakes Based on Fuzzy Control Algorithm

Research On Water Level Control of The Great Lakes Based on Fuzzy Control Algorithm

Socioeconomic spatial analysis through fuzzy system as a tool for territorial planning applied to watersheds

ABSTRACT The watersheds are fundamental in the management of social policies, allowing for the measurement of physical and socioeconomic characteristics through indicators such as the HDI and SII. This study aims to develop the Socioeconomic Analysis Indicator (SAI) for the sub-basins of the Sorocabuçu River (SRB), using a fuzzy system and geoprocessing tools for zoning information by sub-basins. The HDI of the SRB resulted in high values, with income showing the lowest indices, followed by education and longevity. The SII showed low values, with income and literacy significantly contributing to socioeconomic vulnerability. The SAI results indicated reasonable socioeconomic conditions, with lower values in the sub-basins farther from urban and urbanized areas. This study provides data for the establishment of public policies aimed at reducing social and economic inequalities, prioritizing the most vulnerable sub-basins. The data obtained serve as an effective tool to support public management, promoting decisions that improve the quality of life of the local population through the integration of socioeconomic variables for a more holistic and sustainable approach to watershed management.

Finite-time fuzzy adaptive consensus control for state-constrained MIMO nonlinear MASs with switched topologies

In this article, the adaptive fuzzy finite-time output feedback consensus control problem is investigated for multiple input and multiple output (MIMO) nonlinear multi-agent systems (MASs) with switched topologies and asymmetric state constraints. Fuzzy logic systems (FLSs) are utilised to approximate uncertain nonlinear dynamics, and a distributed observer and a state observer are established to estimate the unknown leader and system states, respectively. By constructing the barrier Lyapunov functions (BLFs), a finite-time output feedback consensus control scheme is presented via backstepping control technique. It is proved that the controlled MASs are stable, and the consensus errors converge in finite time. Moreover, the system states will not exceed the given bounds. Finally, the proposed finite-time consensus control approach is applied to control multiple unmanned surface vehicles (USVs), the simulation results check the feasibility of the developed finite-time consensus control methodology.

Prediction of wear properties of CaO and MgO doped stabilized zirconia ceramics produced with different pressing methods using adaptive neuro fuzzy inference systems

AbstractThe present paper describes the fabrication and wear behaviour of CaO and MgO added stabilized zirconia (ZrO2) ceramics produced by powder metallurgy method were examined and modelling with artificial neural networks was studied using the experimental data obtained. CaO/MgO added stabilized zirconia ceramics were fabricated by using a combined method of ball milling, cold pressing ‐ cold isostatic pressing and sintering. CaO and MgO in different amounts (0–8 %mole) were mixed with zirconia. These mixtures were prepared by mechanical alloying method. The green compacts were sintered at 1600 °C. The wear experimental results obtained were converted into data suitable for modelling with artificial neural networks. Wear Load, wear time, CaO and MgO data were used as artificial neural networks input variables. The amount of wear according to the pressing method was taken as the output variables of artificial neural networks. An artificial neural networks was established for the prediction of wear properties of zirconia pressed using the adaptive neuro fuzzy inference systems (ANFIS) learning technique. As a result, a high R2 value of 0.9187 for cold pressing samples and 0,9449 for cold isostatic pressing samples was achieved based on the approach of comparing the success of the model with the test data set and the result produced.

A low-complexity fixed-time prescribed performance control for uncertain full-vehicle active suspensions with hydraulic actuators

This paper addresses the low-complexity fixed-time prescribed performance control problem for uncertain full-vehicle active suspension systems. Different from existing results that ignore the dynamics of the actuator, this paper considers a hydraulic actuator in the controller design. To address the nonlinearities of hydraulic active suspension systems, a new low-complexity fixed-time prescribed performance control method is proposed. In this method, the function approximators (e.g. neural networks (NNs) and fuzzy systems (FSs)) are not needed, which means that the heavy computational costs are avoided. Furthermore, by developing a fixed-time performance function, the proposed method can ensure that the suspension motions converge to the prescribed range within a finite time. Based on the Lyapunov theorem and Extreme Value Theorem, the stability of the closed-loop suspension system is strictly proved. Finally, the comparative simulation results show that the proposed method effectively improves the attitude stability and ride comfort of the suspension system.

A Fuzzy Inference System for Predicting Air Traffic Demand based on Socioeconomic Drivers

The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a Graphical User Interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed Air Passenger Demand Prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

The Application of Big Data and Fuzzy Decision Support Systems in the Innovation of Personalized Music Teaching in Universities

Personalized music teaching in universities improves students’ learning and efficiency through adaptive guidance. This adaptability requires large study data and intelligent decisions based on the learner’s ability. This article introduces a Definitive Teaching Support System (DTSS) exclusive to music learning to augment this concept. This system is designed to increase the adaptability of music learning based on student interest and ability. The system is powered by a fuzzy decision system for identifying maximum teaching adaptability to personalized processes. Low-to-high-sorted personalization provides new endorsements for further music sessions in the fuzzy derivative process. Maximum adaptability is the target for new personalized sessions in the universities. This differs for various students from which a common adaptability level for monotonous recommendations is identified. The identified adaptability is set as a global maximum solution towards music learning personalization. The defuzzification reduces the chances of low adaptability by expelling the stationary adaptability outcomes.

An advanced robust integral reinforcement learning scheme with the fuzzy inference system

An advanced robust integral reinforcement learning scheme with the fuzzy inference system

Application of fuzzy control algorithm in music culture communication and teaching management

Application of fuzzy control algorithm in music culture communication and teaching management

Digital development of manufacturing industry in Yangtze River Delta based on fuzzy control model

Digital development of manufacturing industry in Yangtze River Delta based on fuzzy control model