Adaptive Type-2 Research Articles

Adaptive type-1 fuzzy control of a wind energy conversion system based on a double-fed induction machine

Adaptive type-1 fuzzy control of a wind energy conversion system based on a double-fed induction machine

Design and development of brain emotional learning based adaptive membership functions for type-2 fuzzy systems

This paper presents the design and development of Brain Emotional Learning based adaptive Type-2 Fuzzy Systems for control of dynamical systems. The BEL controller belongs to the class of bio inspired controllers, as its architecture is based on limbic system of human brain and is capable of providing solutions for complex real time problems. In this work, dynamics of Brain Emotional Learning are used for the adaptation of membership functions in the design of Type-2 Fuzzy Logic Controllers. The stability of the overall system is analysed through Lyapunov Yakubovich’s criteria. The proposed approach is validated on the benchmark system such as inverted pendulum, CSTR and Ship heading control through simulation and in real-time environment using OPAL RT OP5600.

Fused LISS IV Image Classification using Deep Convolution Neural Networks

These days, earth observation frameworks give a large number of heterogeneous remote sensing information. The most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. Considering optical Very High Spatial Resolution (VHSR) images, satellites acquire both Multi Spectral (MS) and panchromatic (PAN) images at various spatial goals. Information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. Classification of remote sensing image by Deep learning techniques using Convolutional Neural Networks (CNN) is increasing a solid decent footing because of promising outcomes. The most significant attribute of CNN-based strategies is that earlier element extraction is not required which prompts great speculation capacities. In this article, we are proposing a novel Deep learning based SMDTR-CNN (Same Model with Different Training Round with Convolution Neural Network) approach for classifying fused (LISS IV + PAN) image next to image fusion. The fusion of remote sensing images from CARTOSAT-1 (PAN image) and IRS P6 (LISS IV image) sensor is obtained by Quantization Index Modulation with Discrete Contourlet Transform (QIM-DCT). For enhancing the image fusion execution, we remove specific commotions utilizing Bayesian channel by Adaptive Type-2 Fuzzy System. The outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. The results revealed that SMDTR-CNN with Deep Learning got the best all-around precision and kappa coefficient. Likewise, the accuracy of each class of fused images in LISS IV + PAN dataset is improved by 2% and 5%, respectively.

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller.

The present study aims to design a robust adaptive controller employed in the active tuned mass damper (ATMD) system to overcome undesirable vibrations in multistory buildings under seismic excitations. We propose a novel adaptive type-2 neural-fuzzy controller (AT2NF). All system parameters are taken as unknowns. The MLP neural network is used to extract the Jacobian and estimate the structural model; then, the estimated model is applied to the controller online. To tune the control force applied to the ATMD and achieve the control targets, the controller parameters are adaptively trained using the extended Kalman Filter (EKF) and the error back-propagation algorithm. A PID controller is also included in this method to increase the stability and robustness of the adaptive type-2 neural-fuzzy controller against seismic vibrations. An online simple adaptive controller (OSAC) is studied to demonstrate the suggested controller's superiority. The OSAC is based on adaptive control of the implicit reference model. In this proposed method, the EKF is used to tune the controller parameters online as a novel feature. The uncertainty associated with identifying the mechanical properties of structures, such as mass and stiffness, is one of the primary challenges in the real-time control of structures. This paper investigates how both controllers cope with parametric uncertainties under far-field and near-field seismic excitation. According to numerical results, the AT2NF controller outperforms OSAC in minimizing the dynamic responses of the structure during an earthquake and accomplishing control objectives when the structure's characteristics change.

Trapezoidal type-2 fuzzy inference system with tensor unfolding structure learning method

In the paper, trapezoidal type-2 fuzzy inference system (TT2FIS) is introduced. To construct the type-2 fuzzy inference system, trapezoidal type-2 fuzzy sets are adopted to construct the antecedent part of the fuzzy rules, the centers of the fuzzy sets are roughly clustered by an evolving autonomous data partitioning algorithm. To further eliminate the effect of the centers that are generated by dataset itself, and it can be easily impacted by the unbalance data, the data clouds that are generated by autonomous data partitioning algorithm are filtered by Hammersley sequence, and symmetrical trapezoidal type-2 fuzzy sets are generated by the generated cluster centers and problem-free standard deviation generating method. To the consequent part, generalized type-2 TSK consequent is formulated by tensor, which is obtained via three constituent parts of the trapezoidal type-2 fuzzy sets (lower membership function, upper membership function and type-reduction set of data samples), the parameters of consequent part are the iterative results of a matrix equation that is unfolded from the tensor. Finally, simulation results are carried out to verify the effectiveness of the proposed trapezoidal type-2 fuzzy inference system. Simulation results show that the generalization of the TT2FIS is better than the coincide type-2 fuzzy inference system or adaptive type-1 fuzzy inference system.

Improved Sunflower Optimization Algorithm Tuned Adaptive Type-2 Fuzzy PID Controller for Frequency Regulation of Renewable Power Integrated Hybrid Distributed Power System

Improved Sunflower Optimization Algorithm Tuned Adaptive Type-2 Fuzzy PID Controller for Frequency Regulation of Renewable Power Integrated Hybrid Distributed Power System

Fault Tolerant Control of Robot Manipulators Based on Adaptive Fuzzy Type-2 Backstepping in Attendance of Payload Variation

Fault Tolerant Control of Robot Manipulators Based on Adaptive Fuzzy Type-2 Backstepping in Attendance of Payload Variation

Adaptive Type-2 Fuzzy PID LFC for an Interconnected Power System Considering Input Time-Delay

This paper addresses the designing of an adaptive type-2 fuzzy PID (T2FPID) controller for load frequency control (LFC) of an interconnected nonlinear power system considering input time-delays. The input delay, which can commonly be raised by failure in the communication links and actuator operating lag may lead to control system instability. In the proposed adaptive T2FPID controller, output scaling factors have been chosen judiciously (according to a stable tuning algorithm) and thus improves the stability and performance of the controlled system. To perform this, the stability of the proposed method has been examined using the Lyapunov–Krasovskii (L-K) theorem, and the adaptation laws of the output scaling factors are derived. The proposed adaptation laws for the output scaling factors are simple and need a little knowledge about the structure of the power system. To demonstrate the efficiency of the proposed adaptive T2FPID controller, a comparison to the non-adaptive T2FPID and type-1 FPID (T1FPID) controllers is given for a two-area interconnected nonlinear power system with input time-delays. The obtained simulation results prove that the performance of the proposed T2FPID controller is superior compared to the other controllers in the presence of time-varying input delays, nonlinearities, and uncertainties.

Adaptive Interval Type-2 Fuzzy Filter: An AI Agent for Handling Uncertainties to Preserve Image Naturalness

Artificial intelligence (AI) offers fuzzy set theory (FST) as one of the popular AI agents and decision making tools for digital image processing to increase the robustness of vision-based applications. FST is capable to handle uncertainties while enhancing image quality and preserving its naturalness. This article proposes a novel Adaptive Interval Type-2 Fuzzy Filter (AIT2FF) as an AI agent for preserving the naturalness of nonuniformly illuminated images. The proposed AIT2FF estimates coarse illumination of the input image, which is further processed to obtain the reflectance and refined coarse illumination for the composition of enhanced image. The estimated coarse illumination preserves the naturalness by eliminating uncertainties due to grayness ambiguities in homogeneous regions and spatial ambiguities at edges. The effectiveness of the proposed filter has been presented quantitatively in terms of lightness order error (LOE) and naturalness image quality evaluator (NIQE) score on images from the high dynamic range dataset and images captured with commercial digital cameras. The qualitative comparison visualizes that the enhanced images obtained using the proposed filter maintains visual realism and naturalness. The applications of the proposed filter have also been presented for image dehazing, vehicle tracking, and gradients estimation. Moreover, the detection results for dehazed images have been compared with state-of-the-art methods on real-world task driven testing set from REalistic Single Image DEhazing- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> dataset. The comparisons demonstrate that the enhanced images obtained using the proposed AIT2FF approach are better and outperform others in terms of LOE and NIQE measures. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—The objective of vision-based applications is to automate the human visual perception. This requires a preprocessed input image with fine details. The image details to achieve human vision suffer from uncertainties in homogeneous regions and at edges. This result in an unnatural preprocessed image and degrades the performance. For example: Noisy images are not good to train deep learning models. AI offers one of the best tools, i.e., fuzzy set theory, which is capable to handle uncertainties. This article proposes an adaptive interval Type-2 fuzzy filter for elimination of uncertainties. The proposed filter enhances the image details and maintains its natural appearance. The elimination of uncertainties using proposed filter drops the error value from 2.573 to 1.929 for nonuniformly illuminated images. The proposed approach is able to boost the performance of various vision-based applications like object detection, vehicle tracking, security, medical diagnostics, etc.

Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem.

Model-free adaptive control for the PEMFC air supply system based on interval type-2 fuzzy logic systems

Control aims to avoid oxygen starvation and maximize the net power output by maintaining the optimal oxygen excess ratio (OER), which varies between 1.8 and 2.5. Because of the nonlinearity of the proton exchange membrane fuel cell (PEMFC) air supply system and the different conditions, ensuring an optimal OER is still a challenge. In this study, a model-free adaptive controller is presented for the PEMFC air supply system based on feedback linearization and interval type-2 fuzzy logic systems (IT2 FLSs). Theoretical analysis and experimental results verify the effectiveness of the proposed control scheme. For the theoretical analysis, first, the PEMFC air supply system is transformed into a canonical form with the feedback linearization technique. Then, zero-dynamics stability is discussed in detail to determine the stability of the internal dynamics. Finally, an adaptive interval type-2 fuzzy logic system controller (AIT2FLSC) is designed on the basis of the Lyapunov stability theory, which does not require complete a priori knowledge of the system dynamics. For the experimental results, the root mean square error (RMSE), variance, and standard deviation (SD) of the tracking error are used as tracking performance metrics to evaluate the control accuracy of the proposed AIT2FLSC, which are 0.0968, 0.0093, and 0.0962, respectively. Compared with the traditional proportion integration differentiation controller (RMSE 0.1119, variance 0.0122, and SD 0.1105), this proposed algorithm obtains better adaptability and the RMSE of the tracking error improves 13.48%. Compared with the adaptive type-1 fuzzy logic system controller (AT1FLSC) (RMSE 0.1076, variance 0.0113, and SD 0.1063), this AT2FLSC has a stronger ability to deal with uncertainty and the RMSE of the tracking error improves 10% when the stack temperature is fixed (353.15 K). Furthermore, when the stack temperature is time-varying, the RMSE, variance, and SD of the tracking error under the AIT2FLSC are 0.0966, 0.0092, and 0.0960, respectively, which is less than AT1FLSC (0.1085, 0.0115, and 0.1073) and the RMSE of the tracking error improves 10.99%.

Adaptive Type-2 Fuzzy Neural-Network Control for Teleoperation Systems With Delay and Uncertainties

Interacting with human operators, remote environment, and communication networks, teleoperation systems are considerably suffering from complexities and uncertainties. Managing these is of paramount importance for safe and smooth performance of teleoperation systems. Among the countless solutions developed by researchers, type-2 fuzzy (T2F) algorithms have shown an outstanding performance in modeling complex systems and tackling uncertainties. Moreover, artificial neural networks (NNs) are well known for their adaptive learning potentials. This article proposes an adaptive interval type-2 fuzzy neural-network control scheme for teleoperation systems with time-varying delays and uncertainties. The T2F models are developed based on the experimental data collected from a teleoperation setup over a local computer network. However, the resulted controller is evaluated on an intercontinental communication network through the Internet between Australia and Scotland. Moreover, the slave robot and the remote workspace are completely different and unforeseen. Stability and performance of the proposed control is analyzed by Lyapunov–Krasovskii method. Comprehensive comparative studies demonstrate that the proposed controller outperforms traditional techniques in experimental evaluations.

A novel QIM-DCT based fusion approach for classification of remote sensing images via PSO and SVM models

Fusion of panchromatic and multispectral images has become a research interest for the classification of remote sensing images. The spectral and spatial resolutions of different images give better information with the aid of image classification. However, fusing pixels for various satellite images is difficult due to the nature of original image consists of complex information. Similarly, most of the existing fusion algorithms implement a unified processing over the whole part of the image, thereby leaving certain important needs out of consideration. The main aim of our proposed approach is to fuse the images by gathering all important information from multiple images with minimum errors. In this paper, we propose a novel quantization index modulation with discrete contourlet transform-based fusion approach for classification of remote sensing images (LISS IV sensor). In order to improve the image fusion performance, we eliminate certain noises (salt, pepper, and Gaussian) using Bayesian filter with Adaptive Type-2 Fuzzy System. After image fusion, we make image classification by two steps of processes including deep multi-feature extraction and feature selection. Multiple features such as spectral, shape, global and local features are extracted using Affine Transformation (0??, 90??, 180??, and 270??), and then the best set of features are chosen by mutual information and maximal information coefficients. Finally, the image is classified into seven classes using PSO and SVM namely Urban, Vegetation, Wetland, Tank, Water Area, Bare Land, and Roadways. MATLAB R2017b has been used for evaluation of the LISS IV images. Experimental results revealed that our proposed approach is very effective in terms of their classification accuracy.

Lyapunov approach based design of a gain adaptive interval type-2 fuzzy controller for servo systems

This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.

Commercial Vehicle-Based Robust Control of Seated Whole-Body Vibration Using Adaptive Indirect Type-2 Fuzzy Neural Network

Drivers of commercial vehicles are invariably subject to chronic diseases such as back pain as a result of exposure to severe cabin excitations. In this paper, a six degrees of freedom (6-DOF) coupled human-body and seat suspension system is modeled in order to reduce the vibrations transmitted to the head, seat, and the relative seat and cabin floor displacement. The contributions of the present paper are: 1) two significant but inherently conflicting control objectives are employed, namely the seat acceleration and the relative displacement between seat and cabin floor to account for the effect of seat endstops, 2) A novel learning rate gradient descent based neural network approximator algorithm coupled to an adaptive indirect type-2 fuzzy neural network (T2FNN) controller to converge the controller to the ideal parameters of the uncertain model. 3) The controller model takes into account the seat suspension nonlinearities due to the nonlinear asymmetric piecewise damper and the cubic hardening of the suspension spring. The proposed controller employs the principle of type-2 fuzzy systems with interval membership function and unknown specifications. The effectiveness of the closed-loop system is validated regarding the uncertainties compared to observer-based sliding mode controller (SMC) and a high-fidelity virtual lab MSC.ADAMS-Simulink platform to validate the results in practical scenarios.

Adaptive Fuzzy Type-2 Synergetic Control Based on Bat Optimization for Multi-Machine Power System Stabilizers

Adaptive Fuzzy Type-2 Synergetic Control Based on Bat Optimization for Multi-Machine Power System Stabilizers

Adaptive Fuzzy Type-2 Synergetic Control Based on Bat Optimization for Multi-Machine Power System Stabilizers

A new, adaptive, fuzzy type-2 fast terminal, synergetic multi-machine power system stabilizer is proposed in this study, based on the Bat algorithm. The time spent to reach the equilibrium point, from any initial state, is guaranteed to be finite. The adaptive fuzzy type-2 design is applied to estimate the unknown functions of a multi-machine power system. The parameters of the fast terminal synergetic control are optimized, using bat metaheuristic method. In order to test the robustness of the proposed stabilizer, three load conditions, of the multi-machine power system are studied. A comparison of the proposed adaptive fuzzy type-2 synergetic power system stabilizer with bat conventional approach is presented, indicating improved performance. The control system stability is assessed by the second theorem of Lyapunov and is proven to be asymptotically stable.

Adaptive Type-2 Fuzzy Control for Regulation of Glucose Level in Type 1 Diabetes

In this paper, an adaptive fuzzy controller is presented to regulate the glucose level in type-1 diabetic patients. The dynamics of the glucose-insulin are estimated by the type-2 fuzzy systems. The parameters of the type-2 fuzzy systems are adjusted based on the adaptation laws that are obtained from the Lyapunov stability analysis. In order to evaluate the proposed controller’s performance, the modified Bergman model is considered for a number of diabetic patients under various conditions. Also, the parameters of the modified Bergman model are considered as time-varying and it is shown that the proposed control scheme results in good performance. Finally, the performance of the proposed method is compared with some other techniques.

Adaptive Type-2 Fuzzy Approach for Filtering Salt and Pepper Noise in Grayscale Images

This paper proposes a novel adaptive Type-2 fuzzy filter for removing salt and pepper noise from the images. The filter removes noise in two steps. In the first step, the pixels are categorized as good or bad based on their primary membership function (MF) values in the respective filter window. In this paper, two approaches have been proposed for finding threshold between good or bad pixels by designing primary MFs. a) MFs with distinct Means and same Variance and b) MFs with distinct Means and distinct Variances. The primary MFs of the Type-2 fuzzy set is chosen as Gaussian membership functions. Whereas, in the second step, the pixels categorized as bad are denoised. For denoising, a novel Type-1 fuzzy approach based on a weighted mean of good pixels is presented in the paper. The proposed filter is validated for several standard images with the noise level as low as 20% to as high as 99%. The results show that the proposed filter performs better in terms of peak signal-noise-ratio values compared to other state-of-the-art algorithms.

Adaptive Type-2 Fuzzy Logic Control Applied to the Inverted Pendulum on a Cart Problem

This paper introduces the design of an adaptive interval type-2 fuzzy logic controller and its implementation on a pendulum-cart system. It includes the theoretical foundations of: type-2 fuzzy logic system, input-output linearization control, fuzzy systems for functions approximation and adaptive systems. Type-2 fuzzy controllers provide an approach for using wide variety of opinions in control design. The above allows minimizing the effect of uncertainties in the rule base of fuzzy logic systems. These types of controllers provide greater flexibility in the application of human knowledge during the design of control systems in contrast to traditional controllers employing type-1 fuzzy logic. We propose to compare an adaptive interval type-2 fuzzy logic controller with its corresponding adaptive type-1 fuzzy logic controller. These controllers are tested in real-time on an inverted pendulum on a cart experimental prototype under the presence of parametric uncertainties in the mass of the pendulum. Five performance indexes are computed to compare both controllers, showing that type-2 fuzzy systems can help to improve the response of type-1 fuzzy systems. Copyright © 2018 Praise Worthy Prize - All rights reserved.