| 1. |
- Aslam, Muhammad Shamrooz, et al.
(författare)
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Observer–Based Control for a New Stochastic Maximum Power Point tracking for Photovoltaic Systems With Networked Control System
- 2023
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Ingår i: IEEE transactions on fuzzy systems. - Piscataway, NJ : IEEE. - 1063-6706 .- 1941-0034. ; 31:6, s. 1870-1884
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Tidskriftsartikel (refereegranskat)abstract
- This study discusses the new stochastic maximum power point tracking (MPPT) control approach towards the photovoltaic cells (PCs). PC generator is isolated from the grid, resulting in a direct current (DC) microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed a Networked Control Systems (NCSs) beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the Takagi–Sugeno (T–S) fuzzy model is another option. Takagi–Sugeno (T–S) fuzzy model is presented in a unified framework, for which 1) the fuzzy observer–based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer–based controller can be created using this same premises be the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulating in the PC generator model for the realtime climate data obtained in China demonstrates the importance of our method. In addition, by using a new Lyapunov–Krasovskii functional (LKF) for combining to the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the T-P transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators. © 2022 IEEE.
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| 2. |
- Cao, Bin, et al.
(författare)
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Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression Programming
- 2022
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Ingår i: IEEE transactions on fuzzy systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6706 .- 1941-0034. ; 30:10, s. 4190-4200
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Tidskriftsartikel (refereegranskat)abstract
- The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability.
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| 3. |
- Emami, Reza, et al.
(författare)
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Development of a systematic methodology of fuzzy logic modeling
- 1998
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Ingår i: IEEE transactions on fuzzy systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6706 .- 1941-0034. ; 6:3, s. 346-361
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposs a systematic methodology of fuzzy logic modeling as a generic tool for modeling of complex systems. The methodology conveys three distinct features: 1) a unified parameterized reasoning formulation; 2) an improved fuzzy clustering algorithm; and 3) an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces four parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. Unlike traditional approach of selecting the inference mechanism a priori, the fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering algorithm. Major bottle-necks of the algorithm are addressed and analytical solutions are suggested. Furthermore, we also address the classification process in fuzzy modelng to extend the derived fuzzy partition to the entire output space. This issue remains unattained in the current literature. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy line clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples - a nonlinear function and a gas furnace dynamic procedure - are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches. © 1998 IEEE.
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| 4. |
- Herman, Pawel, et al.
(författare)
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Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain-Computer Interface Classification of Motor Imagery Induced EEG Patterns
- 2017
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Ingår i: IEEE transactions on fuzzy systems. - : IEEE Press. - 1063-6706 .- 1941-0034. ; 25:1, s. 29-42
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Tidskriftsartikel (refereegranskat)abstract
- One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain-computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data-generating mechanism. The objective of this work is, thus, to examine the applicability of the T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: 1) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery, and 2) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis, kernel Fisher discriminant, and support vector machines as well as a conventional type-1 FLS, simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification.
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| 5. |
- Kadmiry, Bourhane, et al.
(författare)
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A fuzzy gain-scheduler for the attitude control of an unmanned helicopter
- 2004
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Ingår i: IEEE transactions on fuzzy systems. - : IEEE Computer Society. - 1063-6706 .- 1941-0034. ; 12:4, s. 502-515
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we address the design of an attitude controller that achieves stable, and robust aggressive maneuverability for an unmanned helicopter. The controller proposed is in the form of a fuzzy gain-scheduler, and is used for stable and robust altitude, roll, pitch, and yaw control. The controller is obtained from a realistic nonlinear multiple-input-multiple-output model of a real unmanned helicopter platform, the APID-MK3. The results of this work are illustrated by extensive simulation, showing that the objective of aggressive, and robust maneuverability has been achieved.
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| 6. |
- Liao, Qianfang, 1983-, et al.
(författare)
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Interaction Measures for Control Configuration Selection Based on Interval Type-2 Takagi-Sugeno Fuzzy Model
- 2018
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Ingår i: IEEE transactions on fuzzy systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6706 .- 1941-0034. ; 26:5, s. 2510-2523
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Tidskriftsartikel (refereegranskat)abstract
- Interaction measure determines decentralized and parse control configurations for a multivariable process control. This paper investigates interval type-2 Takagi–Sugeno fuzzy (IT2TSF) model based interactionmeasures using two different criteria, one is controllability and observability gramians, the other is relative normalized gain array (RNGA). The main contributions are: first, a data-driven IT2TSF modeling method is introduced; econd, explicit formulas to execute the two measures based on IT2TSF models are given; third, two interaction indexes are defined from RNGA to select sparse control configuration; fourth, the calculations to derive sensitivities of the two measures with respect to parametric variations in the IT2TSF models are developed; and fifth, the discussion to compare the two measures is presented. Three multivariable processes are used as examples to show that the results calculated from IT2TSF models are more accurate than that from their type-1 counterparts, and compared to gramian-basedmeasure, RNGA selectsmore reasonable control configurations and is more robust to the parametric uncertainties.
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| 7. |
- Narukawa, Yasuo, et al.
(författare)
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Scores for hesitant fuzzy sets : aggregation functions and generalized integrals
- 2023
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Ingår i: IEEE transactions on fuzzy systems. - : IEEE. - 1063-6706 .- 1941-0034. ; 31:7, s. 2425-2434
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Tidskriftsartikel (refereegranskat)abstract
- There are several extensions of fuzzy sets. Hesitant fuzzy sets are one of them. They are defined in terms of a set of membership degrees. For a typical hesitant fuzzy set, this set of membership degrees has a finite number of values. One of the motivations to introduce score functions was to rank alternatives. In this case, as the membership degrees are a set, the comparison of membership values does not lead, in general, to a total order. Score functions can be seen as functions that transform the set of membership degrees into a single membership value. In this way, we can construct the total order. In this article, we propose a general framework to define score functions for hesitant fuzzy sets based on fuzzy integrals. This framework permits to see most relevant indices as particular cases. Moreover, previous approaches focused on typical hesitant fuzzy sets. Our approach is more general in the sense that we can process both typical hesitant fuzzy sets and the nontypical ones (with membership values that are not finite). We also frame the problem into a more general setting. That is, the problem of hesitant fuzzy set transformation. Score functions can be seen as functions that transform a hesitant fuzzy set into a standard fuzzy set. Similarly, we can consider its transformation to interval-valued fuzzy sets and type-2 fuzzy sets. Aggregation functions can also be used for the same purpose.
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| 8. |
- Nie, Linlin, et al.
(författare)
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Improved Nonlinear Extended Observer Based Adaptive Fuzzy Output Feedback Control for a Class of Uncertain Nonlinear Systems With Unknown Input Hysteresis
- 2023
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Ingår i: IEEE Transactions on Fuzzy Systems. - 1941-0034 .- 1063-6706. ; 31:10, s. 3679-3689
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Tidskriftsartikel (refereegranskat)abstract
- This study focuses on the problem of adaptive fuzzy dynamic surface output feedback control for a class of uncertain nonlinear systems subjected to unknown input hysteresis. A Prandtl-Ishlinskii (PI) model is applied to the uncertain nonlinear system for describing the unknown input hysteresis, making the controller design feasible. In addition, a nonlinear extended state observer (NESO) is designed for simultaneously estimating the unmeasurable states and generalized disturbances, including the nonlinear hysteresis term of the PI model and external disturbances. In addition, a novel nonlinear function is designed to replace fal(·) function of the general NESO to address a modification that increases the convergence speed. Considering the incorporation of the improved nonlinear extended state observer (INESO), an adaptive output feedback control scheme is proposed based on fuzzy logic system and dynamic surface techniques. A command filter is employed to avoid the 'explosion of complexity' problem inherent in the backstepping technique, while compensating the filtering error caused by adopting the filter. The Lyapunov approach is used to demonstrate the stability of the entire closed-loop system. Experiments regarding a piezoelectric micropositioning stage are conducted, the results of which illustrate that the proposed adaptive fuzzy output feedback control method can guarantee a satisfactory tracking performance.
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| 9. |
- Sun, Da, 1989-, et al.
(författare)
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A Framework of Robot Manipulability Learning and Control and Its Application in Telerobotics
- 2024
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Ingår i: IEEE transactions on fuzzy systems. - : IEEE. - 1063-6706 .- 1941-0034. ; 32:1, s. 266-280
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Tidskriftsartikel (refereegranskat)abstract
- Manipulability ellipsoid on the Riemannian manifold serves as an effective criterion to analyze, measure, and control the dexterous performance of robots. For asymmetric bilateral telerobotics, due to the different structures of master and slave robots, it is difficult or even impossible for the operator to manually regulate the manipulability of the remote slave robot. Thus, it is desired that the slave robot can automatically regulate its manipulability to assist the operator in remote different task executions, like humans regulating their own postures to enhance manipulability and adapt to different task scenarios. This article proposes a novel framework for manipulability transfer from human to robot. In this framework, we develop a Type-2 fuzzy model-based imitation learning method to encode and reproduce manipulability ellipsoids from demonstrations. This method can achieve high performance in accuracy and computational efficiency. In addition, it supports learning from a single demonstration. Then, we combine this method with a Riemannian manifold-based quadratic programming control algorithm such that the robot manipulability can fast track the desired manipulability profile. This framework is applied to telerobotics, in which a bilateral teleoperation controller is designed that enables the robot to follow the operator's command and simultaneously self-regulate its manipulability to perform the task adaptively. Meanwhile, the operator can receive force feedback relating to the manipulability regulation. Evaluations using comparative studies and practical experiments with a 3-DoF haptic device and 7-DoF robots are presented to show the effectiveness of the proposed framework.
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| 10. |
- Sun, Da, 1989-, et al.
(författare)
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A Fuzzy Cluster-based Framework for Robot-Environment Collision Reaction
- 2024
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Ingår i: IEEE transactions on fuzzy systems. - : IEEE. - 1063-6706 .- 1941-0034. ; 32:1, s. 75-89
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Tidskriftsartikel (refereegranskat)abstract
- Environmental collision is a challenging issue in human-robot collaboration. This article proposes a novel fuzzy cluster-based framework for robots to have reactive responses to various environmental collision scenarios. This framework makes four contributions: First, a fuzzy cluster-based environmental collision detection algorithm is developed to efficiently classify the collision area and non-collision (free) area of the environment. Second, based on the collision detection algorithm, a p-norm approximation-based collision avoidance algorithm is proposed to enable robots to avoid environmental collisions with guaranteed stability. Third, by extending the collision avoidance algorithm, an environmental collision adaptation algorithm is proposed to allow robots to adapt to environmental collisions with intelligently regulated contact force. Fourth, a teleoperation controller is designed to strengthen haptic force rendering and enhance the operator’s perception of collisions. Going beyond existing methods, the proposed framework allows teleoperated robots to have real-time responses to collisions in quasi-static environments without suffering from local optima, where the environments can be unstructured, non-convex, and detected with noisy outliers. In addition, this framework is simple in implementation because the proposed collision avoidance and collision adaptation algorithms work as several linear Quadratic Programming (QP) constraints that can be flexibly used by Inverse Kinematics (IK) solvers. Several experiments using 7-Degree of Freedom (DoF) robots are conducted to test and compare the proposed framework with existing methods, demonstrating the effectiveness of our work.
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