Fuzzy Dynamic Model Research Articles

Enterprise ERP system optimization based on deep learning and dynamic fuzzy model

Enterprise ERP system optimization based on deep learning and dynamic fuzzy model

Delayed Fuzzy Output Feedback H∞ Control for Offshore Structures

Vibration damping of jacket platforms is among the significant issues in marine science and engineering, and the design of active vibration control schemes is very important to ensure the stability and safety of the jacket platforms against external loadings. This paper provides three fuzzy output feedback H ∞ controllers of the jacket platforms for irregular wave forces. By considering time-varying masses of jacket platforms, a Takagi-Sugeno (T-S) fuzzy dynamic model of the structure is established. Then fuzzy output feedback H ∞ control schemes are developed via using output signals of the platform with current and/or are delayed. Several existence conditions of fuzzy output feedback H ∞ controllers are derived. Simulation results demonstrate that the fuzzy output feedback H ∞ control strategies are remarkable to suppress the vibration of structure. Moreover, by choosing proper delayed output information of the system, the presented delayed fuzzy output feedback H ∞ control schemes outperform the conventional fuzzy output feedback H ∞ control approach.

Optimal Design of Adaptive Robust Control for the Delta Robot with Uncertainty: Fuzzy Set-Based Approach

An optimal control design for the uncertain Delta robot is proposed in the paper. The uncertain factors of the Delta robot include the unknown dynamic parameters, the residual vibration disturbances and the nonlinear joints friction, which are (possibly fast) time-varying and bounded. A fuzzy set theoretic approach is creatively used to describe the system uncertainty. With the fuzzily depicted uncertainty, an adaptive robust control, based on the fuzzy dynamic model, is established. It designs an adaptation mechanism, consisting of the leakage term and the dead-zone, to estimate the uncertainty information. An optimal design is constructed for the Delta robot and solved by minimizing a fuzzy set-based performance index. Unlike the traditional fuzzy control methods (if-then rules-based), the proposed control scheme is deterministic and fuzzily optimized. It is proven that the global solution in the closed form for this optimal design always exists and is unique. This research provides the Delta parallel robot a novel optimal control to guarantee the system performance regardless of the uncertainty. The effectiveness of the proposed control is illustrated by a series of simulation experiments. The results reveal that the further applications in other robots are feasible.

A new insight into implementing Mamdani fuzzy inference system for dynamic process modeling: Application on flash separator fuzzy dynamic modeling

In this work, a novel approach to model the dynamic behavior of the flash separation process (as a main building block of non-reacting stage-wise operations) based on Mamdani Fuzzy Inference Systems is proposed. This model surmounts the need to solve various types of mathematical equations governing the system and does not require thermodynamic properties which are either not available or computationally demanding. Hence it can be easily used in dynamic simulation of multi-phase flow in distributed systems. In the proposed approach the overall model is broken into several simple sub-models based on intuitive analysis of an expert. Moreover, a new fuzzy concept, named “Linguistic Composition Variable” is introduced to represent components mole fractions of each phase as a fuzzy variable. Accordingly, large number of rules which is the main shortcoming of the Mamdani Fuzzy system is significantly reduced.The performance of the proposed dynamic model is evaluated through comparing its results against their corresponding counterparts for a flash separator of crude oil. Overall MAPE (Mean Absolute Percentage Error) values of 7.17% for the gas molar fractions, 3.06% for liquid molar fractions, 10.16% for the temperature, 0.63% for the pressure and 16.44% for the liquid level of the separator have been achieved showing that the proposed fuzzy model can effectively capture the general trends of process data of the dynamic process.

Robust control design of electric helicopter tail reduction system: Fuzzy and optimal view

The optimal robust control with a fuzzy approach is applied to design the electric helicopter tail reduction system in this article. Firstly, the fuzzy dynamical model of the electric helicopter tail reduction system with uncertainties and external disturbances is established, which may be time varying. Then, we propose a deterministic robust controller (differing from IF-THEN rules in traditional fuzzy control) to solve the uncertain problem in electric helicopter tail reduction systems. The electromechanical system with the proposed controller is proved to be stable via the Lyapunov function. The control gain with an optimal design is considered, which minimizes a fuzzy performance index associated with both the control error and the control cost. Furthermore, simulations compared with linear-matrix-inequality control are made to validate the effectiveness and stability of the optimal robust controller. All results show that the performance of the fuzzy electric helicopter tail reduction system can be always guaranteed by this optimal robust control approach.

A Fuzzy Dynamic Optimal Model for COVID-19 Epidemic in India Based on Granular Differentiability

The pandemic SARS-CoV-2 has become an undying virus to spread sustainable disease named COVID-19 for upcoming few years. The mortality rate of the disease is increasing rapidly as the approved drug is not available yet. Isolation from the infected individual or community is the recommended choice to save our existence. As the human is the only carrier so in that case if the host carriers are isolated from each other then it might be possible to control the spread or positive rates of infected population. Whereas only isolation might not be the only recommended solution. These are the resolutions of previous research work carried out on COVID-19 throughout the world. The present scenario of the world and public health is knocking hard with a big question of critical uncertainty of COVID-19 because of its imprecise database as per daily positive cases recorded all over the world and in India as well. In this research work we have presented an optimal control model for COVID-19 by using fuzzy dynamical system based granular differentiability. In the first step, we have formulated the fuzzy SEIAHRD model for COVID-19, analysed using granular differentiability and reported the disease dynamics for time independent disease control parameter. In the second step, we have upgraded the concerned fuzzy dynamical system and granular differentiability model up to an optimal control problem invader with time dependent control parameter. The theoretical findings are validated graphically with some realistic data for pandemic COVID-19 with respect to India’s perspective.

Evaluation of Renewable Energy Technology Based on Reliability Attributes Using Hybrid Fuzzy Dynamic Decision-Making Model

Socio-technical and economic attributes consideration are very important during a renewable energy technology selection for a community. When decision-makers considered these attributes under a dynamic nature, they arrive at a robust decision. Hence, this study proposes an integrated model for renewable energy technologies evaluation under a dynamic condition. We developed the model using dynamic intuitionistic fuzzy Einstein geometric averaging operator, intuitionistic fuzzy entropy, and the intuitionistic fuzzy technique for order of preference by similarity to ideal solution method (TOPSIS). This model’s applicability was tested using five renewable energy technologies - solar (PT1), wind (PT2), hydroelectricity (PT3), geothermal (PT4) and biomass (PT5) and five attributes (risk factor, payback reliability, social benefit, change in demand and cost). Based on five energy experts, from academia and industry, opinions, the proposed model identified biomass energy technology as the most suitable energy technology. Three existing multi-criteria models were used to verify the proposed model; the proposed model performance was consistent with the existing models’ results. From most suitable the least suitable, the model ranked these technologies PT5 > PT2 > PT3 > PT1 > PT4.

Performance-Based Fault Detection and Fault-Tolerant Control for Nonlinear Systems With T-S Fuzzy Implementation.

This article addresses the performance-based fault detection (FD) and fault-tolerant control (FTC) issues for nonlinear systems. For this purpose, in the first part of this article, the performance-based FD and FTC scheme is investigated with the aid of the nonlinear factorization technique. To be specific, the controller parameterization for nonlinear systems is first discussed. The so-called fault-tolerant margin is introduced as an indicator of the system fault-tolerant ability. Then, the FD scheme aiming at estimating and detecting the stability performance degradation of the closed-loop system caused by the system faults is developed. Furthermore, to recover the system performance, the performance-based FTC strategy is presented. In the second part of this article, the design approach of the performance-based FD and FTC scheme is studied by applying the Takagi-Sugeno fuzzy dynamic modeling technique. The achieved results are demonstrated in the end by a case study on the three-tank system.

Fuzzy dynamic models of situation analysis, decision support, and control in complex systems

In this work we consider the theoretical foundations of building fuzzy models of system dynamics, analysis of situations, their development, and control in complex weakly-structured systems based on knowledge modeling and expert preferences. The proposed fuzzy models can serve as mathematical tool for analyzing the behavior of a system in various situations, as well as to serve as the basis of specialized computer simulation systems of decision support in unstructured fuzzy situations. As is known, decision making problems under uncertainty can be divided into two groups: decision making in static situations and decision making in dynamic situations. For decision making in static situations, the methods and models of decision support based on the theory of choice are developed. For decision-making in dynamic situations, the dynamic models, including those based on expert knowledge, are used. To build a dynamic model of a situation, the expert knowledge about physical, economic, or social processes occurring in a system, which are represented as a cognitive map, is employed. The models of analysis of static situations are focused on evaluation and ordering of alternatives, and models of analysis of dynamic situations are focused on generating of strategies (alternatives) to achieve the targets of control, that is, the desired target state of the situation. Thus, one of the aims of this paper is to develop mathematical methods for constructing integrated decision support models based on fuzzy models of hierarchical estimation, control, and fuzzy cognitive modeling.

Dynamic fuzzy data envelopment analysis models: case of bus transport performance assessment

In the transport field, two characteristics–inter-temporal dependency and fuzziness–need to be considered when assessing transport performance. First, input and output levels are inter-temporal dependent due to heavy capital investment and because quasi-fixed input can influence output levels over multiple periods. Second, conventional Data Envelopment Analysis (DEA) models are, in nature, formulated with quantitative variables. However, qualitative measurements that are characterized with “vagueness” or “fuzziness” are as important as quantitative variables for multi-period transport performance assessment. To rectify these problems, the present study extends previous research by proposing a Dynamic Fuzzy Data Envelopment Analysis (DFDEA) method for assessing the comparative efficiency where inter-temporal dependence exists in operating production processes with some “fuzzy” variables. An case study was conducted to evaluate the performance of city bus transport companies in Taipei, Taiwan. Results showed the superiority of the proposed DFDEA model by comparing the results with static models.

Fuzzy-Model-Based Output Feedback Sliding-Mode Control for Discrete-Time Uncertain Nonlinear Systems

This paper addresses the output feedback sliding-mode control (SMC) problem for discrete-time uncertain nonlinear systems through Takagi-Sugeno fuzzy dynamic models. Combining with the sliding surface, a descriptor system is constructed to characterize the sliding motion dynamics. Sufficient conditions for asymptotic stability analysis of the sliding motion are attained by the piecewise quadratic Lyapunov functions within a convex optimization setup. Two SMC design approaches are proposed to ensure the finite-time convergence of the sliding surface. Two simulation examples are presented to show the effectiveness of the proposed approaches.

A Fuzzy Optimal Control Inventory Model of Product–Process Innovation and Fuzzy Learning Effect in Finite Time Horizon

In this study, we have introduced a fuzzy dynamic optimal control model of process–product innovation with learning by doing. The firm’s cost functions of product and process innovation depend on investment in both the innovations and the gathering of knowledge of product and process innovation. In this model, the product price, and the investments of product and process innovation are control variables; the product quality, production cost, and the change rates of gathering knowledge accumulations of product and process innovation are state variables. To represent the model more realistically, we considered all the variables are fuzzy in nature. The main goal of this report is to probe the relationships between these variables and investigate the optimality criteria for the model. The model is formulated as a single objective profit maximization problem in the single period finite time horizon and solved numerically using Runge–Kutta forward–backward method of fourth order using MATLAB software. Further, some numerical experiments are performed and the graphical representation of the results are also depicted to illustrate the model. Also a sensitivity analysis is conducted to study the issue of varying the parameters and coefficients on the target function value.

Multi-dimensional dynamic fuzzy monitoring model for the effect of water pollution treatment.

The rapid development of the economy in China resulted in increasingly serious water pollution problem. A lot of water pollution treatment projects have been launched to improve the water environment quality. Water pollution treatment is a complex and long-term task. Considering the concept of water pollution with fuzziness and the factors affecting the effect of water pollution treatment (EWPT), this study constructs a multi-dimensional dynamic fuzzy comprehensive monitoring model. The model considers the vague boundaries in the representation of water pollution and various factors affecting the treatment effect, such as monitoring time, monitoring index, and monitoring location. In detail, firstly, existing methods for evaluating the EWPT are analyzed and reviewed. Then a multi-dimensional dynamic model is developed for monitoring the EWPT. Finally, the Yueya Lake of Henan Province in China is taken as an example to demonstrate the effectiveness and practicability of the proposed method. From the analysis of the results, to maintain the cleanliness of the water, efforts should still be made to eliminate and completely block the pollutants on the shore in order to fundamentally solve the problem.

Revised convexity, normality and stability properties of the dynamical feedback fuzzy state space model (FFSSM) of insulin–glucose regulatory system in humans

This research tries to explore more important structural properties of the insulin–glucose regulatory system in humans. Consequently, an important theorem, namely “revised modified optimized defuzzified value theorem” for feedback systems is derived and then proved. Moreover, the properties concerning the convexity, normality and the bounded-input bounded-output stability of the induced solution of FFSSM are researched. The proposed theorems and lemmas are successfully implemented and verified for the insulin–glucose system in humans. The successful and promising results and proofs of the theorems of the relevant properties improve the credibility and reliability of the FFSSM model of the insulin–glucose regulatory system in humans.

Initial state perturbations as a validation method for data-driven fuzzy models of cellular networks

BackgroundData-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in computational biology. Since measurements are prone to errors, approaches dealing with uncertain data are especially suitable for this task. Fuzzy models are one such approach, but they contain a large amount of parameters and are thus susceptible to over-fitting. Validation methods that help detect over-fitting are therefore needed to eliminate inaccurate models.ResultsWe propose a method to enlarge the validation datasets on which a fuzzy dynamic model of a cellular network can be tested. We apply our method to two data-driven dynamic models of the MAPK signalling pathway and two models of the mammalian circadian clock. We show that random initial state perturbations can drastically increase the mean error of predictions of an inaccurate computational model, while keeping errors of predictions of accurate models small.ConclusionsWith the improvement of validation methods, fuzzy models are becoming more accurate and are thus likely to gain new applications. This field of research is promising not only because fuzzy models can cope with uncertainty, but also because their run time is short compared to conventional modelling methods that are nowadays used in systems biology.

Application of fuzzy dynamic weights drivability evaluation model in tip-in condition

Only the subjective or objective method is employed to calculate the index weight in the evaluation of drivability, which makes the evaluation result less accurate. In this paper, a drivability evaluation model was proposed on the basis of fuzzy dynamic weights, and the drivability was evaluated in tip-in conditions. The evaluation indexes affecting drivability in tip-in conditions were determined using multi-dimensional analysis. The dynamic weight was built using an improved analytic hierarchy process and improved entropy method in accordance with the principle of “function driving and difference driving.” The fuzzy matrix was built by drawing upon the improved membership function of the evaluation index. Finally, the drivability evaluation model of the tip-in condition was built with dynamic weight and fuzzy matrix. The evaluation model was employed to evaluate the drivability of the vehicle, which obtained the drivability score. As the experiment results suggest, the evaluation model is accurate and effective in comparison with the real score and the score obtained from the fuzzy analytic hierarchy process. The evaluation model is employed to formulate a control strategy for a vehicle that will allow better drivability. Besides, the method makes the performance development of the vehicle more knowledgeable and agile.

English

English

Clear and Fuzzy Neighborhood Models of Wastewater Treatment

The paper describes the definition of wastewater and their main types, shows the main components of the sewage treatment plant. The process of wastewater treatment is presented using clear and fuzzy dynamic neighborhood models, their comparative characteristics are given, and a conclusion is drawn.

Development of Genetically tuned Fuzzy dynamic model for nonlinear dynamical systems: Application on reaction section of Tennessee Eastman process

This work presents a new GA-Fuzzy method to model dynamic behavior of a process, based on Recurrent Fuzzy modeling through Mamdani approach whose inference system is optimized by Genetic Algorithms. By using the Mamdani approach, the proposed method surmounts the need to solve various types of mathematical equations governing the dynamic behavior of the process. The proposed method consists of two steps; i) constructing a startup version of the model, ii) optimizing the shape of membership functions of the fuzzy sets corresponding to the variables exist in the fuzzy model, along with the production rules constituting the inference such that the obtained fuzzy model can predict the dynamic behavior of the process fairly accurately. The proposed method is used to predict the dynamic behavior of the reaction section of the Tennessee Eastman (TE) benchmark. The overall accuracy of the obtained results compared to their corresponding counterparts in TE benchmark. The mean absolute percentage error (MAPE) of the key process variables which are temperature, pressure, and level of the reactor, and the reactor cooling water outlet temperature were calculated as 1.17%, 0.38%, 1.5%, and 1.57%, respectively, showing high prediction capability of the proposed method.

A Fuzzy Approach for Optimal Robust Control Design of an Automotive Electronic Throttle System

In this paper, we propose a fuzzy approach for optimal robust control design of an automotive electronic throttle (ET) system. Compared with the conventional ET control systems, we establish the fuzzy dynamical model of the ET system with parameter uncertainties, nonlinearities, and external disturbances, which may be nonlinear, (possibly fast) time varying. These uncertainties are assumed to be bounded, and the knowledge of the bound only lies within a prescribed fuzzy set. A robust control that is deterministic and is not the usual if–then rules-based control is presented to guarantee the controlled system to achieve the deterministic performance: uniform boundedness and uniform ultimate boundedness. Furthermore, a fuzzy-based system performance index including average fuzzy system performance and control cost is proposed based on the fuzzy information. The optimal design problem associated with the control can then be solved by minimizing the fuzzy-based performance index. With this optimal robust control, the performance of the fuzzy ET system is both deterministically guaranteed and fuzzily optimized.