Fuzzy Dynamic Model Research Articles

Fault Detection Observer Design for Nonlinear Systems via Fuzzy Lyapunov Functions

This article focuses on the design problem of fault detection (FD) observer for a class of continuous-time nonlinear systems. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H_{-}}$ </tex-math></inline-formula> index is employed to measure the worst cast fault sensitivity. First, the nonlinear systems are represented by the Takagi–Sugeno (T–S) fuzzy dynamic models. Then, a novel fuzzy Lyapunov function (FLF) analyzing approach is proposed to reduce the conservatism in stability analysis and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H_{-}}$ </tex-math></inline-formula> index estimation for the T–S fuzzy model. In particular, employing this new type of the FLF, the stability analysis of the T–S fuzzy systems perfectly overcomes the difficulty of evaluating the upper bound of the time derivative of the membership function. Based on the descriptor system approach, some new sufficient conditions on the existence of fuzzy FD observers are obtained in terms of linear matrix inequalities (LMIs). Finally, illustrative examples are given to show the effectiveness of the proposed method.

Iterative learning model predictive control for multivariable nonlinear batch processes based on dynamic fuzzy PLS model

This paper proposes a latent variable nonlinear iterative learning model predictive control method (LV-NILMPC) based on the dynamic fuzzy partial least squares (DFPLS) model to achieve trajectory tracking and process disturbance suppression in multivariable nonlinear batch processes. The dynamic and nonlinear characteristics of the physical system are constructed by integrating the T-S fuzzy model into the regression framework of the dynamic partial least squares (PLS) inner model. The decoupling and dimensionality reduction characteristics of the DFPLS model automatically decompose a multivariable nonlinear system into multiple univariate subsystems operating independently in the latent variable space. Based on the DFPLS model, we design LV-NILMPC controllers corresponding to each latent variable subspace to track the projection of the reference trajectories. Compared with the previous control method, the method proposed in this paper has a faster convergence rate and smaller tracking error. The method is suitable for nonlinear, multivariable and strong coupling batch processes. Finally, the application of two cases shows that the method is effective.

Design of memory-based adaptive integral sliding-mode controller for fractional-order T-S fuzzy systems and its applications

This study investigates the passivity analysis of fractional-order Takagi-Sugeno (T-S) fuzzy systems subject to external disturbances and nonlinear perturbations under an adaptive integral sliding mode control (AISMC) methodology. To better accommodate the features of the T-S fuzzy dynamical model, a novel fractional-order memory-based integral-type sliding manifold function is defined, which is different from the existing sliding manifold function. With the help of Caputo fractional-order derivative properties and quadratic Lyapunov functional, some linear matrix inequality (LMI)-based sufficient criteria are derived to ensure the asymptotic stability conditions of resulting sliding mode dynamics with passive performance index. Besides that, an adaptive sliding mode control law is designed for the addressed systems to guarantee the system state variables onto the predefined integral sliding manifold. Finally, the effectiveness of the proposed controller is validated based on derived sufficient conditions with two practical models, such as fractional-order interconnected power systems and fractional-order permanent-magnet synchronous generator (PMSG) model, respectively.

Dynamic event-triggered security control for networked T-S fuzzy system with non-uniform sampling

This paper is focused on the security control problem for the dynamic event-triggered networked T-S fuzzy control system with non-uniform sampling and stochastic network attacks. Different from existing research, the signals from sensor are sampled in a non-uniform period. To improve communication, a novel dynamic event-triggered method is proposed, which can adjust the trigger parameters dynamically according to the changes of external output. Then, a T-S fuzzy dynamic output feedback control model with event-triggered mechanism and network attacks is constructed. Furthermore, by introducing a set of slack matrix, the asynchronous premise variable problem caused by event-triggered method is solved, and some sufficient conditions that insure the asymptotic stability with an H∞ performance requirement of the established closed-loop system can be obtained by utilizing Lyapunov function method and stochastic analysis technique. Moreover, the gains of the fuzzy dynamic output feedback controllers are found by solving the linear matrix inequalities (LMIs). Finally, two practical examples are given to support the merits and effectiveness of the proposed method.

Hybrid Fuzzy Dynamic Model for the Evaluation of Energy Aggregation Strategy

Hybrid Fuzzy Dynamic Model for the Evaluation of Energy Aggregation Strategy

Fuzzy Dynamic Thermal Rating System-Based Thermal Aging Model for Transmission Lines

Electricity demand has surged over the last several years and will persist in the future. Increased transmission loads cause transmission lines to operate much closer to their security limits, leading to thermal and mechanical stress and thus affecting the transmission reliability and thermal aging. Accordingly, monitoring the conductor temperature over time is critical to identifying power transmission networks that may need extra attention and perhaps maintenance. This paper presents a fuzzy thermal aging model for transmission lines equipped with a fuzzy dynamic thermal rating system based on the IEEE 738 standard. In this framework, the ampacity of the transmission line was calculated. The conductor temperature was computed with the back-calculation method by considering the fully loaded transmission line. The estimated conductor temperature was employed to determine the corresponding conductor fuzzy loss of tensile strength, i.e., the fuzzy annealing degree of the conductor based on the Harvey model. Additionally, a tensile strength loss cost profile is provided. Simulation and numerical results indicate that the proposed framework is robust against various operating conditions of the parameters considered in the study and provides crucial information for managing transmission assets and transmission network operation.

Robust constraint-following control for permanent magnet linear motor with optimal design: A fuzzy approach

In this paper, robust constraint-following control (RCFC) with the optimal design is developed to handle the trajectory tracking control issues for permanent magnet linear motor (PMLM), which control performance is deteriorated mainly by friction, ripple force, and external disturbance. Specifically, a fuzzy description for the main nonlinearity of the PMLM system and its fuzzy dynamic model is formulated. Then, the tracking specification is modeled as a performance constraint, the RCFC algorithm following the Udwadia-Kalaba theory is designed to comply with this constraint, and possess strong robustness to uncertainties simultaneously. The resulting controller is demonstrated to be uniform bounded and uniform ultimate bounded with Lyapunov analysis. Furthermore, the optimal design issue via a fuzzy approach is investigated to achieve the optimal tradeoff between control effort and system performance. Finally, the rapid control prototype platform CSPACE is employed to implement the real-time control while avoiding time-consuming repetitive programming and debugging. Simulation and experimental results illustrate the actual effectiveness of the proposed algorithm.

Dynamic programming approach for fuzzy linear programming problems FLPs and its application to optimal resource allocation problems in education system

This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the established dynamic programming model for solving linear programming problems in a crisp environment. The mentioned approach is upgraded to address the problem in an uncertain environment. Dynamic programming model can either be passing forward or backward. In the proposed approach backward dynamic programming approach is adopted to address the problem. It is then followed by implementing the proposed method on the education system of Pakistan. The education system of Pakistan comprises of the Primary, Middle, Secondary, and Tertiary education stages. The problem is to maximize the efficiency of the education system while achieving the targets with minimum usage of the constrained resources. Likewise the model tries to maximize the enrollment in the Primary, Middle, Secondary and Tertiary educational categories, subject to the total available resources in a fuzzy uncertain environment. The solution proposes that the enrollment can be increased by an amount 9997130, by increasing the enrollment in the Middle and Tertiary educational categories. Thus the proposed method contributes to increase the objective function value by 30%. Moreover, the proposed solutions violate none of the constraints. In other words, the problem of resources allocation in education system is efficiently managed to increase efficiency while remaining in the available constrained resources. The motivation behind using the dynamic programming methodology is that it always possesses a numerical solution, unlike the other approaches having no solution at certain times. The proposed fuzzy model takes into account uncertainty in the linear programming modeling process and is more robust, flexible and practicable.

An sEMG based adaptive method for human-exoskeleton collaboration in variable walking environments

In this paper, a novel long short memory network – adaptive robust iterative learning control (LSTM-ARILC) framework is proposed to achieve accurate continuous motion estimation and adaptive following control for the exoskeleton robot, which is applied to offer assistance to human walking in variable environments. The LSTM network is established to estimate the human lower limb motion through the processed surface Electromyography (sEMG) signals, and the human-exoskeleton coupling fuzzy dynamic model is further developed. Then the closed-loop ARILC controller is designed to compensate the estimated errors and realize the adaptive and robust following control, and its asymptotic stability is rigorously proved via Lyapunov theory. The performance of the proposed method is evaluated with numerical experiments and compared with adaptive PID controller and adaptive fuzzy sliding mode controller (SMC). The maximum following errors of ARILC after 6 iterations can be reduced by more than 99% compared to the initial iteration, and the controller is quite less sensitive to the environment changes than the other two controllers, which proves that the proposed control framework is much more robust and effective in variable walking environments.

Sliding mode observer based control for T-S fuzzy descriptor systems

&lt;p style='text-indent:20px;'&gt;In this paper, the problem of sliding mode observer (SMO) based sliding mode control (SMC) for nonlinear descriptor delay systems is studied. First, based on the T-S fuzzy dynamic modeling technique, the nonlinear descriptor system is transformed into a combination of local linear models. Then, a integral-type sliding surface (ITSS) based SMO is constructed for the error system. In the sequel, sufficient linear matrix inequality (LMI) conditions are established to ensure the admissibility of the sliding motions and obtain the observer gain matrix. Furthermore, two novel SMC laws are developed to ensure the reachability conditions and stabilize the descriptor systems. Finally, simulations are provided to show the effectiveness of the method.&lt;/p&gt;

Finite-time sliding mode control for UVMS via T-S fuzzy approach

&lt;p style='text-indent:20px;'&gt;In order to solve the control problem of Underwater Vehicle with Manipulator System (UVMS), this paper proposes a finite-time sliding mode control strategy via T-S fuzzy approach. From the general dynamic model of UVMS and considering the influence between the manipulator and the underwater vehicle, hydrodynamic damping, buoyancy and gravity as the fuzzy items, we establish global fuzzy dynamic model and design a closed-loop fuzzy sliding mode controller. We prove the model in theory from two aspects: the reachability of sliding domain and the finite-time boundedness. We also give the solution of the controller gain. A simulation on the actual four joint dynamic model of UVMS with two fuzzy subsystems is carried out to verify the effectiveness of this method.&lt;/p&gt;

Identification of Policies Based on Assessment-Optimization Model to Confront Vulnerable Resources System with Large Population Scale in a Big City.

The conflict between excessive population development and vulnerable resource (including water, food, and energy resources) capacity influenced by multiple uncertainties can increase the difficulty of decision making in a big city with large population scale. In this study, an adaptive population and water–food–energy (WFE) management framework (APRF) incorporating vulnerability assessment, uncertainty analysis, and systemic optimization methods is developed for optimizing the relationship between population development and WFE management (P-WFE) under combined policies. In the APRF, the vulnerability of WFE was calculated by an entropy-based driver–pressure–state–response (E-DPSR) model to reflect the exposure, sensitivity, and adaptability caused by population growth, economic development, and resource governance. Meanwhile, a scenario-based dynamic fuzzy model with Hurwicz criterion (SDFH) is proposed for not only optimizing the relationship of P-WFE with uncertain information expressed as possibility and probability distributions, but also reflecting the risk preference of policymakers with an elected manner. The developed APRF is applied to a real case study of Beijing city, which has characteristics of a large population scale and resource deficit. The results of WFE shortages and population adjustments were obtained to identify an optimized P-WEF plan under various policies, to support the adjustment of the current policy in Beijing city. Meanwhile, the results associated with resource vulnerability and benefit analysis were analyzed for improving the robustness of policy generation.

Event-triggered fault detection for nonlinear descriptor networked control systems

This paper investigates the problem of filter-based fault detection (FD) for stochastic nonlinear descriptor networked control systems (NCSs). Based on the T-S fuzzy dynamic modeling technique, the nonlinear descriptor NCS is transformed into a combination of local linear models. Then, to save the network resources, a new adaptive event-triggered mechanism is presented to determine whether the sampled data is to be transmitted. Considering network-induced delay, data packet dropout, and asynchronous premise variables, a time-delay augmented system is established to improve the detection performance. The additional item caused by data packet dropout is regarded as a disturbance, and the effect of this part is attenuated by using the H∞ method. A sufficient condition is proposed to guarantee the admissibility of the augmented system, meanwhile, the desired filter parameters are obtained. Finally, simulation results are provided to demonstrate the feasibility of the proposed strategy.

Dynamic fuzzy neighborhood rough set approach for interval-valued information systems with fuzzy decision

Nowadays, many extended rough set models are proposed to acquire valuable knowledge from interval-valued information system. These existing models mainly focus on different forms of similarity relations. However, most of these similarity relations are qualitative rather than quantitative, which is not reasonable in some practical cases. In addition, with the arrival of new objects and the removal of obsolete objects, the interval-valued information system with fuzzy decision (IvIS_FD) is always changing with time. Therefore, how to efficiently mining knowledge from dynamic IvIS_FD is a meaningful topic. Motivated by these two issues, we study the dynamic fuzzy neighborhood rough set approach for IvIS_FD, aiming to effectively update the rough approximations when the IvIS_FD evolves over time. Firstly, δ-fuzzy neighborhood relation is defined to describe the similarity relation between objects quantitatively. Secondly, we introduce a novel fuzzy neighborhood rough set model and its matrix representation suitable for IvIS_FD. On this basis, we discuss the incremental mechanisms to update fuzzy neighborhood approximations when multiple objects are added to or deleted from an IvIS_FD, respectively. Meanwhile, corresponding dynamic algorithms are designed and explained. Finally, experiments are performed on nine public data sets to evaluate the performance of the dynamic fuzzy neighborhood rough set model. Experimental results verify that the proposed model is effective and efficient for updating rough approximations in dynamic IvIS_FD.

A study of dynamic fuzzy cognitive map model with group consensus based on linguistic variables

A fuzzy cognitive map (FCM) is an analysis tool that uses a graph structure to show the causal relationships of influence factors in a decision-making system. During the decision-making process, it is reasonable for experts to use linguistic variables to express their subjective opinions. However, few studies have discussed methods for aggregating the linguistic opinions of experts to reach a group consensus in FCM. In addition, the interaction weights among the factors in FCM will usually change over time in a real environment. Therefore, we applied the learning algorithm to adjust the interaction weights among the factors in the steps of FCM, after which we proposed a dynamic fuzzy cognitive map model with group consensus based on the linguistic evaluations in this study. Finally, we presented a case study using the proposed model to illustrate the development possibility of the Internet of Things (IoT) industry in Taiwan. According to the analysis results, we found that the development prospects for the IoT industry in Taiwan are optimistic. The four key factors for IoT industry development were found to be the degree of authorization and trust, the development of application technologies, the complexity of systems and equipment and cross-platform possibility.

Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization.

Combination therapies proved to be a valuable strategy in the fight against cancer, thanks to their increased efficacy in inducing tumor cell death and in reducing tumor growth, metastatic potential, and the risk of developing drug resistance. The identification of effective combinations of drug targets generally relies on costly and time consuming processes based on in vitro experiments. Here, we present a novel computational approach that, by integrating dynamic fuzzy modeling with multi-objective optimization, allows to efficiently identify novel combination cancer therapies, with a relevant saving in working time and costs. We tested this approach on a model of oncogenic K-ras cancer cells characterized by a marked Warburg effect. The computational approach was validated by its capability in finding out therapies already known in the literature for this type of cancer cell. More importantly, our results show that this method can suggest potential therapies consisting in a small number of molecular targets. In the model of oncogenic K-ras cancer cells, for instance, we identified combination of up to three targets, which affect different cellular pathways that are crucial for cancer proliferation and survival.

A complementary unsupervised load disaggregation method for residential loads at very low sampling rate data

In this paper, low-resolution smart metering data analysis is applied to breakdown the load consumption for household appliances to facilitate the deployment of residential energy management solutions. A complementary NILM approach for smart meter data with a very low sampling rate is designed to disaggregate the energy consumption data for both household space thermal appliances and small appliances. The load disaggregation approach relies only on the general activity time usage data and public statistical data for appliance consumptions. A novel three-step disaggregation topology is applied to complement NILM problems. The first step is separating white appliances loads using existing NILM algorithms (commercial algorithm of WATT-IS company). The second disaggregation step couples an edge detection technique with a k-means cluster method to detect the ON/OFF event for heating and cooling loads, from the residual aggregated loads. Finally, a novel disaggregation approach using a dynamic fuzzy logic model and a predictive method is applied for the remaining aggregated loads to identify the ON/OFF event occurrence for small appliances. The proposed method is validated using French household dataset with 10 min sample data rates. Then it is applied to disaggregate the load consumption for Portuguese household dataset with 15 min sample data rates.

Performance-Oriented Fault Detection for Nonlinear Control Systems via Data-Driven T-S Fuzzy Modeling Technique

This article studies the performance-oriented fault detection (FD) for nonlinear control systems in the data-driven framework. Unlike the traditional residual- or test statistic-based FD methods, the proposed approach focuses on evaluating the system performance changes/degradations caused by the faults. Specifically, the regulation performance described by an infinite-time cost function is first introduced as a control performance index of the feedback system. Using only the process input and output (I/O) data, a Takagi–Sugeno fuzzy dynamic model is constructed with the aid of subspace identification method. Then, the internal system state variables are expressed in terms of the I/O data and a fuzzy cost function is proposed to approximate the performance index. On this basis, the temporal difference error induced by the Bellman equation is adopted as the process evaluation indicator, whose threshold is determined through the randomized algorithm. To demonstrate the effectiveness of the proposed FD approach, case studies are performed in the end on a ship propulsion system and a three-tank system.

Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines

Aiming at the characteristics of the nonlinear changes in the internal corrosion rate in gas pipelines, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a principal component analysis (PCA) algorithm and a dynamic fuzzy neural network (D-FNN) to address the problems above. The principal component analysis algorithm is used for dimensional reduction and feature extraction, and a dynamic fuzzy neural network model is utilized to perform the prediction. The study implementing the PCA-D-FNN is further accomplished with the corrosion data from a real pipeline, and the results are compared among the artificial neural networks, fuzzy neural networks, and D-FNN models. The results verify the effectiveness of the model and algorithm for inner corrosion rate prediction.

Generalized Dynamic Fuzzy NN Model Based on Multiple Fading Factors SCKF and its Application in Integrated Navigation

Traditional neural network (NN)'s generalization ability is weak, and its prediction accuracy depends heavily on the selection of network structure and training samples, so it cannot be directedly applied to the strapdown inertial navigation system (SINS) and global navigation satellite system (GNSS) integrated navigation system in varied environment. Aiming at these two problems, based on the fuzzy neural network (FNN) model, a new neuron growth-attenuation mechanism is established by introducing the dynamic adjustment idea of network structure. Therefore, the network has a compact structure and good performance, which prevents the network from over-training and over-fitting. Besides, the theory of strong tracking filter (STF) is introduced into the nonlinear filter to design the multiple fading factor square root cubature kalman filter (MSCKF) method for neural network parameter training, which shortens the training time and improves the convergence speed. Results of the simulation and physical experiment verification demonstrate that the generalization ability of proposed model is enhanced and prediction accuracy is improved during the GNSS signal loss. Compared with the pure inertial navigation method, the position errors in latitude and longitude are reduced by 85.00%, 89.71% and the velocity errors in east and north are reduced by 94.57%, 83.11%, respectively by the proposed generalized dynamic fuzzy NN Model Based on MSCKF(MSCKF-GDFNN).