Showing papers in "IEEE Transactions on Fuzzy Systems in 2015"
TL;DR: It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds.
Abstract: In this paper, a partial tracking error constrained fuzzy output-feedback dynamic surface control (DSC) scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems. The considered MIMO nonlinear systems contain unknown functions and without the requirement of their states being available for the controller design. With the help of fuzzy logic systems identifying the MIMO unknown nonlinear systems, a fuzzy adaptive observer is established to estimate the unmeasured states. By transforming the tracking errors into new virtual error variables and based on the DSC backstepping recursive design technique, a new adaptive fuzzy output-feedback control method is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds. The simulation results and comparisons with the previous control approaches confirm the effectiveness and utility of the proposed scheme.
475 citations
TL;DR: The main features of the proposed adaptive control approach are that it can guarantee the stability of the closed-loop system, and the tracking errors converge to a small neighborhood of zero, and it can solve the problems of unknown control direction, unknown dead-zone, and unmeasured states simultaneously.
Abstract: In this paper, an adaptive fuzzy backstepping output-feedback tracking control approach is proposed for a class of multi-input and multi-output (MIMO) stochastic nonlinear systems. The MIMO stochastic nonlinear systems under study are assumed to possess unstructured uncertainties, unknown dead-zones, and unknown control directions. By using a linear state transformation, the unknown control coefficients and the unknown slopes characteristic of the dead-zones are lumped together, and the original system is transformed to a new system on which the control design becomes feasible. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By introducing a special Nussbaum gain function into the backstepping control design, a stable adaptive fuzzy output-feedback tracking control scheme is developed. The main features of the proposed adaptive control approach are that it can guarantee the stability of the closed-loop system, and the tracking errors converge to a small neighborhood of zero. Moreover, it can solve the problems of unknown control direction, unknown dead-zone, and unmeasured states simultaneously. Two simulation examples are provided to show the effectiveness of the proposed approach.
410 citations
TL;DR: For the first time, GFHMs are used to approximate the solutions (value functions) of the coupled HJ equations, based on policy iteration algorithm, and the approximation solution is utilized to obtain the optimal coordination control.
Abstract: In this paper, a new online scheme is presented to design the optimal coordination control for the consensus problem of multiagent differential games by fuzzy adaptive dynamic programming, which brings together game theory, generalized fuzzy hyperbolic model (GFHM), and adaptive dynamic programming. In general, the optimal coordination control for multiagent differential games is the solution of the coupled Hamilton-Jacobi (HJ) equations. Here, for the first time, GFHMs are used to approximate the solutions (value functions) of the coupled HJ equations, based on policy iteration algorithm. Namely, for each agent, GFHM is used to capture the mapping between the local consensus error and local value function. Since our scheme uses the single-network architecture for each agent (which eliminates the action network model compared with dual-network architecture), it is a more reasonable architecture for multiagent systems. Furthermore, the approximation solution is utilized to obtain the optimal coordination control. Finally, we give the stability analysis for our scheme, and prove the weight estimation error and the local consensus error are uniformly ultimately bounded. Further, the control node trajectory is proven to be cooperative uniformly ultimately bounded.
371 citations
TL;DR: This paper develops a method to solve the multicriteria decision making (MCDM) problem within the context of HFLTS in which the criteria conflict with each other, and proposes a sort of hesitant fuzzy linguistic measures, which are motivated by the traditional VIKOR method.
Abstract: The hesitant fuzzy linguistic term set (HFLTS) has turned out to be a powerful and flexible technique in representing decision makers’ qualitative assessments in the processes of decision making. The aim of this paper is to develop a method to solve the multicriteria decision making (MCDM) problem within the context of HFLTS in which the criteria conflict with each other. To do so, the concepts of ideal solutions for a HFL-MCDM problem have been introduced. In addition, in order to represent the closeness of one solution to the ideal one, we propose a sort of hesitant fuzzy linguistic measures, such as the hesitant fuzzy linguistic group utility measure, the hesitant fuzzy linguistic individual regret measure, and the hesitant fuzzy linguistic compromise measure. Based on these measures, we develop a hesitant fuzzy linguistic VIKOR (HFL-VIKOR) method, which is motivated by the traditional VIKOR method. The general procedures for the HFL-VIKOR method are given. Some numerical examples are provided to demonstrate the advantages and practicality of our method. Finally, we make some discussions on the advantages of the HFL-VIKOR method, as well as future work.
350 citations
TL;DR: It is shown that the proposed controller guarantees that all signals in the closed-loop system are fourth-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value.
Abstract: This paper focuses on the problem of approximation-based adaptive fuzzy tracking control for a class of stochastic nonlinear time-delay systems with a nonstrict-feedback structure. A variable separation approach is introduced to overcome the design difficulty from the nonstrict-feedback structure. Mamdani-type fuzzy logic systems are utilized to model the unknown nonlinear functions in the process of controller design, and an adaptive fuzzy tracking controller is systematically designed by using a backstepping technique. It is shown that the proposed controller guarantees that all signals in the closed-loop system are fourth-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value. Simulation results are provided to demonstrate the effectiveness of our results. Further developments will consider how to generalize the proposed strategy to nonstrict-feedback nonlinear systems with input nonlinearities.
246 citations
TL;DR: The study makes a claim and offers sound evidence behind the observation that higher fuzziness of a fuzzy classifier may imply better generalization aspects of the classifier, especially for classification data exhibiting complex boundaries.
Abstract: We investigate essential relationships between generalization capabilities and fuzziness of fuzzy classifiers (viz., the classifiers whose outputs are vectors of membership grades of a pattern to the individual classes). The study makes a claim and offers sound evidence behind the observation that higher fuzziness of a fuzzy classifier may imply better generalization aspects of the classifier, especially for classification data exhibiting complex boundaries. This observation is not intuitive with a commonly accepted position in “traditional” pattern recognition. The relationship that obeys the conditional maximum entropy principle is experimentally confirmed. Furthermore, the relationship can be explained by the fact that samples located close to classification boundaries are more difficult to be correctly classified than the samples positioned far from the boundaries. This relationship is expected to provide some guidelines as to the improvement of generalization aspects of fuzzy classifiers.
236 citations
TL;DR: This paper investigates the adaptive fuzzy backstepping control and H∞ performance analysis for a class of nonlinear systems with sampled and delayed measurements and finds the proposed control scheme and stability analysis to be effective.
Abstract: This paper investigates the adaptive fuzzy backstepping control and ${H_\infty}$ performance analysis for a class of nonlinear systems with sampled and delayed measurements. In the control scheme, a fuzzy-estimator (FE) model is used to estimate the states of the controlled plant, while the fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. The controller is obtained based on the FE model by combining the backstepping technique with the classic adaptive fuzzy control method. In the stability analysis, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SUUB) and the outputs of the system are proven to converge to a small neighborhood of origin. Furthermore, the ${H_\infty}$ performance is investigated and the outputs of the closed-loop system are bounded in the ${H_\infty}$ sense. Two examples are given to illustrate the effectiveness of the proposed control scheme.
221 citations
TL;DR: Based on the robust control approach, sufficient conditions are obtained to ensure that the filtering error system is asymptotically stable with a prescribed H∞ performance level and the eigenvalues of the filteringerror system in a given circular region.
Abstract: This paper is concerned with the nonfragile distributed $H_\infty$ filtering problem for a class of discrete-time Takagi–Sugeno (T–S) systems in sensor networks. Additive filter gain uncertainties that reflect imprecision in filter implementation are considered. Based on the robust control approach, sufficient conditions are obtained to ensure that the filtering error system is asymptotically stable with a prescribed $H_\infty$ performance level and the eigenvalues of the filtering error system in a given circular region. The filter parameters are determined by solving a set of linear matrix inequalities. A simulation study on the nonlinear tunnel diode circuit system is presented to show the effectiveness of the proposed design method.
210 citations
TL;DR: The pinning control strategies for networks with continuous-time dynamics to discontinuous networks are extended and the Takagi-Sugeno (T-S) fuzzy interpolation approach is applied, demonstrating that the theoretical results are effective and the T-S fuzzy approach is important for relaxed results.
Abstract: This paper is concerned with the cluster synchronization in finite time for a class of complex networks with nonlinear coupling strengths and probabilistic coupling delays. The complex networks consist of several clusters of nonidentical discontinuous systems suffered from uncertain bounded external disturbance. Based on the Takagi–Sugeno (T–S) fuzzy interpolation approach, we first obtain a set of T–S fuzzy complex networks with constant coupling strengths. By developing some novel Lyapunov functionals and using the concept of Filippov solution, some new analytical techniques are established to derive sufficient conditions ensuring the cluster synchronization in a setting time. In particular, this paper extends the pinning control strategies for networks with continuous-time dynamics to discontinuous networks. Numerical simulations demonstrate that the theoretical results are effective and the T–S fuzzy approach is important for relaxed results.
205 citations
TL;DR: The adaptive fuzzy identification and control problems are considered for a class of multi-input multi-output nonlinear systems with unknown functions and unknown dead-zone inputs and the Lyapunov stability theorem is proved.
Abstract: The adaptive fuzzy identification and control problems are considered for a class of multi-input multi-output nonlinear systems with unknown functions and unknown dead-zone inputs. The main characteristics of the considered systems are that 1) they are composed of n subsystems and each subsystem is in nested lower triangular form, 2) dead-zone inputs are in nonsymmetric nonlinear form, and 3) dead-zone inputs appear nonlinearly in the systems and their parameters are not required to be known. The controller design for this class of systems is a difficult and complicated task because of the existences of unknown functions, the couplings among the nested subsystems, and the dead-zone inputs. In the controller design, the fuzzy logic systems are employed to approximate the unknown functions and the differential mean value theorem is used to separate dead-zone inputs. To compensate for dead-zone inputs, the compensative terms are designed in the controllers. The stability of the closed-loop system is proved via the Lyapunov stability theorem. A simulation example is provided to validate the feasibility of the approach.
204 citations
TL;DR: For a high-order considered system, the attention is focused on the construction of a reduced-order model, which not only approximates the original system well with a Hankel-norm performance but translates it into a lower dimensional fuzzy switched system as well.
Abstract: In this paper, the model approximation problem is investigated for a Takagi–Sugeno fuzzy switched system with stochastic disturbance. For a high-order considered system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with a Hankel-norm performance but translates it into a lower dimensional fuzzy switched system as well. By using the average dwell time approach and the piecewise Lyapunov function technique, a sufficient condition is first proposed to guarantee the mean-square exponential stability with a Hankel-norm error performance for the error system. The model approximation is then converted into a convex optimization problem by using a linearization procedure. Finally, simulations are provided to illustrate the effectiveness of the proposed theory.
TL;DR: Experimental results with a GTRS-based approach on different health care datasets suggest that the approach may improve the overall quality of decision making in the medical field, as well as other fields.
Abstract: The realization of the Web as a common platform, medium, and interface for supporting human activities has attracted many researchers to the study of Web-based support systems (WSS). An important branch of WSS is Web-based decision support systems that provide intelligent support for making effective decisions in different domains. We focus on decision making in Web-based medical decision support systems (WMDSS). Uncertainty is a critical factor that affects decision making and reasoning in the medical field. A three-way decision-making approach is an effective and better choice to lessen the effects of uncertainty. It provides the provision for delaying certain or definite decisions in situations that lack sufficient evidence or accurate information in reaching certain conclusions. Particularly, the option of deferment decisions is added in this approach that provides the flexibility to further examine and investigate the uncertain and doubtful cases. The game-theoretic rough set (GTRS) model is a recent development in rough sets that can be used to determine the three rough set regions in the probabilistic rough sets framework by determining a pair of thresholds. The three regions are used to obtain three-way decision rules in the form of acceptance, rejection, and deferment rules. In this paper, we extend the GTRS model to analyze uncertainty involved in medical decision making. Experimental results with a GTRS-based approach on different health care datasets suggest that the approach may improve the overall quality of decision making in the medical field, as well as other fields. It is hoped that the incorporation of a GTRS component in WMDSS will enrich and enhance its decision-making capabilities.
TL;DR: The fuzzy restricted Boltzmann machine (FRBM) and its learning algorithm are proposed in this paper, in which the parameters governing the model are replaced by fuzzy numbers, which shows that the representation capability of FRBM model is significantly better than the traditional RBM.
Abstract: In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. For regular RBM, the relationships between visible units and hidden units are restricted to be constants. This restriction will certainly downgrade the representation capability of the RBM. To avoid this flaw and enhance deep learning capability, the fuzzy restricted Boltzmann machine (FRBM) and its learning algorithm are proposed in this paper, in which the parameters governing the model are replaced by fuzzy numbers. This way, the original RBM becomes a special case in the FRBM, when there is no fuzziness in the FRBM model. In the process of learning FRBM, the fuzzy free energy function is defuzzified before the probability is defined. The experimental results based on bar-and-stripe benchmark inpainting and MNIST handwritten digits classification problems show that the representation capability of FRBM model is significantly better than the traditional RBM. Additionally, the FRBM also reveals better robustness property compared with RBM when the training data are contaminated by noises.
TL;DR: This paper investigates the problem of filter design for interval type-2 (IT2) fuzzy systems with D stability constraints based on a new performance index by designing a novel type of IT2 filter such that the filtering error system guarantees the prescribed H∞, L2 -L ∞, passive, and dissipativity performance levels withD stability constraints.
Abstract: This paper investigates the problem of filter design for interval type-2 (IT2) fuzzy systems with D stability constraints based on a new performance index. Attention is focused on solving the $H_{\infty}$ , $L_{2}$ – $L_{\infty}$ , passive, and dissipativity fuzzy filter design problems for IT2 fuzzy systems with D stability constraints in a unified frame. Under the new performance index frame, using Lyapunov stability theory, a novel type of IT2 filter is designed such that the filtering error system guarantees the prescribed $H_{\infty}$ , $L_{2}$ – $L_{\infty}$ , passive, and dissipativity performance levels with D stability constraints. The existence condition of the IT2 filter is expressed as the convex optimization problem, and the filter parameters in the condition can be solved by the standard software. The IT2 fuzzy model and IT2 fuzzy filter do not need to share the same lower and upper membership functions. Finally, a numerical example is provided to show the effectiveness of the proposed results.
TL;DR: A class of new convex reliable stabilization conditions are proposed for T-S fuzzy systems using the properties of fuzzy product inference engines, and the obtained result is extended to the H∞ reliable control case.
Abstract: This paper is concerned with reliable state feedback control synthesis for Takagi and Sugeno (T–S) fuzzy systems with sensor multiplicative faults. By considering the influence of sensor faults on both the system states and premise variables of fuzzy controllers, a class of new convex reliable stabilization conditions are proposed for T–S fuzzy systems using the properties of fuzzy product inference engines. Furthermore, the obtained result is extended to the $H_\infty$ reliable control case. The resulting controllers are reliable in that they provide guaranteed asymptotic stability and $H_\infty$ performance when all sensors are operational, as well as when some sensor experiences failures. Different from the proposed approach, the influence of sensor faults on premise variables is not considered in the existing results; then, it might not guarantee the stability and control performance for T–S fuzzy systems with premise variables dependent on the system states. A numerical example is given to illustrate the effectiveness of the proposed method.
TL;DR: F fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs to provide forearm movement assistance so that a human forearm can track any continuous desired trajectory in the presence of parametric/functional uncertainties, unmodeled dynamics, actuator dynamics, and/or disturbances from environments.
Abstract: This paper presents fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs to provide forearm movement assistance so that a human forearm can track any continuous desired trajectory (or constant setpoint) in the presence of parametric/functional uncertainties, unmodeled dynamics, actuator dynamics, and/or disturbances from environments. Given the desired trajectories of human forearm positions, in the developed control, adaptive fuzzy approximators are used to estimate the dynamical uncertainties of the human–robot system, and an iterative learning scheme is utilized to compensate for unknown time-varying periodic disturbances. With the synthesis of the backstepping, iterative learning, and Lyapunov function approaches, the developed controller does not require exact knowledge of the exoskeleton model, and the close-loop system can be proven to be semiglobally uniformly bounded. Three comparison experiments are conducted to illustrate the effectiveness of the proposed control scheme by tracking periodic/repeated trajectories.
TL;DR: A novel risk decision-making method with the aid of HFDTRSs is developed and investigates the ranking and resource allocation by utilizing the associated costs of alternatives and multiobjective 0-1 integer programming.
Abstract: Decision-theoretic rough sets (DTRSs) play a crucial role in risk decision-making problems. With respect to the minimum expected risk, DTRSs deduce the rules of three-way decisions. Considering the new expression of evaluation information with hesitant fuzzy sets (HFSs), we introduce HFSs into DTRSs and explore their decision mechanisms. More specifically, we take into account the losses of DTRSs with hesitant fuzzy elements and propose a new model of hesitant fuzzy decision-theoretic rough sets (HFDTRSs). Some properties of the expected losses and their corresponding scores are carefully investigated under the hesitant fuzzy information. Three-way decisions and the associated cost of each object are further derived. With the above analysis, a novel risk decision-making method with the aid of HFDTRSs is developed. Besides the three-way decisions with DTRSs, the method investigates the ranking and resource allocation by utilizing the associated costs of alternatives and multiobjective 0–1 integer programming. Our study also offers a solution in the aspect of determining losses of DTRS and extends the range of applications.
TL;DR: This paper is concerned with the problem of dissipative control for Takagi-Sugeno fuzzy systems under time-varying sampling with a known upper bound on the sampling intervals and a time-dependent Lyapunov-Krasovskii functional approach is proposed.
Abstract: This paper is concerned with the problem of dissipative control for Takagi–Sugeno fuzzy systems under time-varying sampling with a known upper bound on the sampling intervals. Based on the time-dependent Lyapunov–Krasovskii functional approach, which makes full use of the available information about the actual sampling pattern, a sufficient condition is established to guarantee the sampled-data systems to be exponentially stable and strictly $(\mathcal {Q},\mathcal {S},\mathcal {R})$ - $\gamma$ -dissipative. Based on the criterion, a design algorithm for the desired sampled-data controller is proposed. The effectiveness and benefits of the results developed in this paper is demonstrated by a controller design for a truck-trailer system.
TL;DR: This paper proposes to adapt the inference system of FARC-HD replacing the product t-norm with overlap functions, and defines n-dimensional overlap functions that allow one to obtain more adequate outputs from the base classifiers for the subsequent aggregation in OVO and OVA schemes.
Abstract: There are many real-world classification problems involving multiple classes, e.g., in bioinformatics, computer vision, or medicine. These problems are generally more difficult than their binary counterparts. In this scenario, decomposition strategies usually improve the performance of classifiers. Hence, in this paper, we aim to improve the behavior of fuzzy association rule-based classification model for high-dimensional problems (FARC-HD) fuzzy classifier in multiclass classification problems using decomposition strategies, and more specifically One-versus-One (OVO) and One-versus-All (OVA) strategies. However, when these strategies are applied on FARC-HD, a problem emerges due to the low-confidence values provided by the fuzzy reasoning method. This undesirable condition comes from the application of the product t -norm when computing the matching and association degrees, obtaining low values, which are also dependent on the number of antecedents of the fuzzy rules. As a result, robust aggregation strategies in OVO, such as the weighted voting obtain poor results with this fuzzy classifier. In order to solve these problems, we propose to adapt the inference system of FARC-HD replacing the product t -norm with overlap functions . To do so, we define n-dimensional overlap functions . The usage of these new functions allows one to obtain more adequate outputs from the base classifiers for the subsequent aggregation in OVO and OVA schemes. Furthermore, we propose a new aggregation strategy for OVO to deal with the problem of the weighted voting derived from the inappropriate confidences provided by FARC-HD for this aggregation method. The quality of our new approach is analyzed using 20 datasets and the conclusions are supported by a proper statistical analysis. In order to check the usefulness of our proposal, we carry out a comparison against some of the state-of-the-art fuzzy classifiers. Experimental results show the competitiveness of our method.
TL;DR: This paper presents a compact evolutionary interval-valued fuzzy rule-based classification system with tuning and rule selection with a high degree of interpretability of the generated linguistic model for the modeling and prediction of real-world financial applications.
Abstract: The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS $_{\rm FARC{\hbox{-}}HD}$ ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results.
TL;DR: A novel performance index, which is expressed as an extended dissipativity performance, is introduced to be a generalization of H ∞, L2-L∞, passive, and dissipativity performances indexes.
Abstract: This paper is concerned with the problems of state and output feedback control for interval type-2 (IT2) fuzzy systems with mismatched membership functions. The IT2 fuzzy model and the IT2 state and output feedback controllers do not share the same membership functions. A novel performance index, which is expressed as an extended dissipativity performance, is introduced to be a generalization of $H_{\infty }$ , $L_{2}$ – $L_{\infty }$ , passive, and dissipativity performances indexes. First, the IT2 Takagi–Sugeno fuzzy model and the controllers are constructed by considering the mismatched membership functions. Second, on the basis of Lyapunov stability theory, the IT2 fuzzy state and output feedback controllers are designed, respectively, to guarantee that the closed-loop system is asymptotically stable with extended dissipativity performance. The existence conditions of the two kinds of controllers are obtained in terms of convex optimization problems, which can be solved by standard software. Finally, simulation results are provided to illustrate the effectiveness of the proposed methods.
TL;DR: This paper presents an approach for dynamic maintenance of approximations w.r.t. objects and attributes added simultaneously under the framework of decision-theoretic rough set (DTRS) using equivalence feature vector and matrix and extensive experimental results verify the effectiveness of the proposed methods.
Abstract: Uncertainty and fuzziness generally exist in real-life data. Approximations are employed to describe the uncertain information approximately in rough set theory. Certain and uncertain rules are induced directly from different regions partitioned by approximations. Approximation can further be applied to data-mining-related task, $\hbox{e.g.}$ , attribute reduction. Nowadays, different types of data collected from different applications evolve with time, especially new attributes may appear while new objects are added. This paper presents an approach for dynamic maintenance of approximations $\hbox{w.r.t.}$ objects and attributes added simultaneously under the framework of decision-theoretic rough set (DTRS). Equivalence feature vector and matrix are defined first to update approximations of DTRS in different levels of granularity. Then, the information system is decomposed into subspaces, and the equivalence feature matrix is updated in different subspaces incrementally. Finally, the approximations of DTRS are renewed during the process of updating the equivalence feature matrix. Extensive experimental results verify the effectiveness of the proposed methods.
TL;DR: Simulation results show that the proposed fuzzy sliding mode controller is capable of controlling nonlinear dynamical systems over a network, which is subject to bounded external disturbances, time-varying network-induced delays, and packet losses with adequate performance.
Abstract: Two major challenges in networked control systems are the time-varying networked-induced delays and the packet losses. To alleviate these problems, this study presents a novel fuzzy sliding mode controller, where a fuzzy system is used to estimate the nonlinear dynamical system online, and the networked-induced delay is handled by Pade approximation. The problem of packet losses is handled by viewing them as large time-varying delays in the system. The sliding mode-based design procedure used ensures the stability and the robustness of the proposed controller in the presence of disturbances and time-varying networked-induced time delays. Using an appropriate Lyapunov function, it is proved that the tracking error converges to the neighborhood of zero asymptotically. Furthermore, since the adaptation laws of the parameters are derived by using of the Lyapunov function, these laws are also found to be stable. Simulation results show that the proposed fuzzy sliding mode controller is capable of controlling nonlinear dynamical systems over a network, which is subject to bounded external disturbances, time-varying network-induced delays, and packet losses with adequate performance.
TL;DR: This letter makes some observations about “Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: Towards a wide view on their relationship,” and points out that all operations, methods, and systems that have been developed and published about IT2 FSs are, so far, only valid in the special case when IT2FS = IVFS.
Abstract: This letter makes some observations about “Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: Towards a wide view on their relationship,” IEEE Trans. Fuzzy Systems that further support the distinction between an interval type-2 fuzzy set (IT2 FS) and an interval-valued fuzzy set (IV FS), points out that all operations, methods, and systems that have been developed and published about IT2 FSs are, so far, only valid in the special case when IT2 FS = IVFS, and suggests some research opportunities.
TL;DR: A new optimized algorithm is developed to guarantee the prescribed convergence of tracking error and the boundedness of all the signals in the closed-loop systems by using the proposed output dead-zone model based on Lyapunov synthesis.
Abstract: This paper presents a novel fuzzy adaptive controller for controlling a class of dead-zone output nonlinear systems with time delays. A new approximate model is first designed to describe a special dead-zone phenomenon encountered by the output mechanism of nonlinear systems, and the proposed smooth model can be conveniently fused with available adaptive fuzzy control techniques. In addition, the coupling effect that the dead-zone output and the time-delayed states coexist in a common coupling function makes the tracking control design more complicated. To further address this difficulty, a compensation method using mean-value theorem with Lyapunov–Krasovskii function is presented in this paper. By using the proposed output dead-zone model, and based on Lyapunov synthesis, a new optimized algorithm is developed to guarantee the prescribed convergence of tracking error and the boundedness of all the signals in the closed-loop systems. Simulations have been implemented to verify the performance of the proposed fuzzy adaptive controller.
TL;DR: The idea of using an evolving method as a base for the fault-detection/monitoring system is tested and the results indicate the potential improvement of the WWTP's control during a sensor malfunction.
Abstract: Increasing demands on effluent quality and loads call for an improved control, monitoring, and fault detection of waste-water treatment plants (WWTPs). Improved control and optimization of WWTP lead to increased pollutant removal, a reduced need for chemicals as well as energy savings. An important step toward the optimal functioning of a WWTP is to minimize the influence of sensor faults on the control quality. To achieve this, a fault-detection system should be implemented. In this paper, the idea of using an evolving method as a base for the fault-detection/monitoring system is tested. The system is based on the evolving fuzzy model method. This method allows us to model the nonlinear relations between the variables with the Takagi–Sugeno fuzzy model. The method uses basic evolving mechanisms to add and remove clusters and the adaptation mechanism to adapt the clusters’ and local models’ parameters. The proposed fault-detection system is tested on measured data from a real WWTP. The results indicate the potential improvement of the WWTP's control during a sensor malfunction.
TL;DR: A method with Atanassov's interval-valued intuitionistic linguistic numbers (AIVILNs) based on trapezium clouds is presented, which can provide solutions to multicriteria group decision-making problems with Ataassov’s interval- valued intuitionistic linguistics information.
Abstract: The cloud model, which can implement uncertain transformation between a qualitative concept and its quantitative instantiations, has attracted great attention in multicriteria decision-making problems with linguistic information. This paper proposes some operations and a possibility degree of trapezium clouds, as well as several new aggregation operators: the trapezium cloud weighted arithmetic averaging operator, the trapezium cloud ordered weighted arithmetic averaging operator, and the trapezium cloud hybrid arithmetic operator. Moreover, a method with Atanassov's interval-valued intuitionistic linguistic numbers (AIVILNs) based on trapezium clouds is presented, which can provide solutions to multicriteria group decision-making problems with Atanassov's interval-valued intuitionistic linguistic information. In this method, AIVILNs are first converted into trapezium clouds and aggregated by trapezium cloud aggregation operators. Then, the ranking of alternatives is determined by the possibility degree matrix of trapezium clouds. Finally, an illustrative example confirming the validity and feasibility of the proposed method is given.
TL;DR: It is proved that, even though the dead-zone input ~Γ(u) is fuzzy, the integrated fuzzy controller can make the closed-loop system semiglobally uniformly ultimately bounded and the tracking error converge to a small neighborhood of the origin.
Abstract: This paper focuses on a problem of adaptive control for a class of nonlinear strict-feedback systems with a fuzzy dead zone and immeasurable states. By using the adaptive backstepping technique, an adaptive fuzzy output-feedback controller is constructed. The proposed control method requires only one adaptive law for an nth-order system. Compared with the conventional deterministic dead-zone models in previous articles, the main advantage of this paper is that the proposed dead-zone model is uncertain and fuzzy. By defuzzifying for fuzzy dead zone ~Γ(u) and employing an integrated design, an integrated fuzzy controller is constructed. It is proved that, even though the dead-zone input ~Γ(u) is fuzzy, the integrated fuzzy controller can make the closed-loop system semiglobally uniformly ultimately bounded and the tracking error converge to a small neighborhood of the origin. Finally, simulation results are provided to show the effectiveness of the proposed approach.
TL;DR: A novel design method for adaptive tracking control by employing the direct adaptive fuzzy approximation method to approximate the unknown and desired control input signals instead of the unknown nonlinear functions, the number of adaptive design parameters obtained in the control design process is greatly reduced.
Abstract: In this paper, a novel design method for adaptive tracking control is proposed for robot finger dynamics. First, the dynamics are described by considering the robot finger as a large-scale system since it has many joints and multi-degrees of freedoms (DOFs). Second, by employing the direct adaptive fuzzy approximation method to approximate the unknown and desired control input signals instead of the unknown nonlinear functions, the number of adaptive design parameters obtained in the control design process is greatly reduced. Moreover, it is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded. Finally, simulations are conducted on the Puma 560 robot manipulator, and the results show the effectiveness of the developed control design approach.
TL;DR: This paper investigates the fault detection and isolation (FDI) problem for a class of nonlinear state-feedback control systems with parameter uncertainties by introducing a switching technique to address the uncertain grades of membership, external disturbances, local nonlinear parts, faults, and the coupling of them.
Abstract: This paper investigates the fault detection and isolation (FDI) problem for a class of nonlinear state-feedback control systems with parameter uncertainties. First, the considered nonlinear systems are described by multiple T-S fuzzy models with uncertain grades of membership and local nonlinear parts in normal case and faulty cases. Then, a switching technique is introduced to address the uncertain grades of membership, external disturbances, local nonlinear parts, faults, and the coupling of them. In a multiple-model scheme, a bank of FDI observers are constructed, each of which is based on the T-S model that describes the system in the presence of a particular fault, such that one of them can track the current system state, and the corresponding observer estimate error can converge exponentially to zero. Finally, two examples are given to illustrate the effectiveness and advantages of the new results.