Fuzzy Switching Research Articles

Study on Intelligent Pulp Consistency Control Strategies

As the pulp consistency system has a big lag behind with uncertain model, the precise control of the pulp consistency could be achieved by making use of the respective advantages of variable domain fuzzy control method and the PID control method based on PSO, and shifting between these two controllers by fuzzy soft handoff switch.

Stabilizing switching control for nonlinear system based on quasi‐ARX RBFN model

AbstractIn this paper, a fuzzy switching adaptive control approach is presented for nonlinear systems. The proposed fuzzy switching adaptive control law is composed of a quasi‐ARX radial basis function network (RBFN) prediction model and a fuzzy switching mechanism. The quasi‐ARX RBFN prediction model consists of two parts: a linear part used for a linear controller to ensure boundedness of the input and output signals; and an RBFN nonlinear part used to improve control accuracy. By using the fuzzy switching scheme between the linear and nonlinear controllers to replace the 0/1 switching, it can realize a better balance between stability and accuracy. Theoretical analysis and simulation results show the effectiveness of the proposed control method on the stability, accuracy, and robustness. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller

In this paper, a new active fault tolerant control (AFTC) methodology is proposed based on a state estimation scheme for fault detection and identification (FDI) to deal with the potential problems due to possible fault scenarios. A bank of adaptive unscented Kalman filters (AUKFs) is used as a core of FDI module. The AUKF approach alleviates the inflexibility of the conventional UKF due to constant covariance set up, leading to probable divergence. A fuzzy-based decision making (FDM) algorithm is introduced to diagnose sensor and/or actuator faults. The proposed FDI approach is utilized to recursively correct the measurement vector and the model used for both state estimation and output prediction in a model predictive control (MPC) formulation. Robustness of the proposed FTC system, H∞ optimal robust controller and MPC are combined via a fuzzy switch that is used for switching between MPC and robust controller such that FTC system is able to maintain the offset free behavior in the face of abrupt changes in model parameters and unmeasured disturbances. This methodology is applied on benchmark three-tank system; the proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated leading to an offset free behavior in the presence of sensor/actuator faults that can be either abrupt or drift change in biases. Analysis of the simulation results reveals that the proposed approach provides an effective method for treating faults (biases/drifts in sensors/actuators, changes in model parameters and unmeasured disturbances) under the unified framework of robust fault tolerant control.

Direct Adaptive Fuzzy Sliding Mode Control with Variable Universe Fuzzy Switching Term for a Class of MIMO Nonlinear Systems

Combining adaptive fuzzy sliding mode control with fuzzy or variable universe fuzzy switching technique, this study develops two novel direct adaptive schemes for a class of MIMO nonlinear systems with uncertainties and external disturbances. The proposed control schemes consist of fuzzy equivalent control terms, fuzzy switching control terms (in scheme one) or variable universe fuzzy switching control terms (in scheme two), and compensation control terms. The compensation control terms are used to relax the assumption on fuzzy approximation error. Based on Lyapunov stability theory, the parameters update laws are adaptively tuned online and the global asymptotic stability of the closed‐loop system can be guaranteed. The major contribution of this study is to develop a novel framework for designing direct adaptive fuzzy sliding mode control scheme facing model uncertainties and external disturbances. The derived schemes can effectively solve the chattering problem and the equivalent control calculation in that environment. Simulation results performed on a two‐link robotic manipulator demonstrate the feasibility of the proposed control schemes.

Fuzzy switching of fuzzy rules for energy management in HEV

Energy management or supervisory control of Hybrid Electric Vehicles (HEV) has been the subject of a large amount of scientific effort in the last years. This paper presents a real time energy management algorithm based on fuzzy rules controller. This strategy has been improved applying a specified method of fuzzy rules switching. This method takes into account the evolution of the state of charge of the storage element at any time; in addition it carries the fuel cell to operate at its best efficiency point. It was verified, if the method of fuzzy switching is applied in real time on any unknown profile, the result is near-optimal.

A novel fuzzy system for edge detection in noisy image using bacterial foraging

Bio-inspired edge detection using fuzzy logic has achieved great attention in the recent years. The bacterial foraging (BF) algorithm, introduced in Passino (IEEE Control Syst Mag 22(3):52---67, 2002) is one of the powerful bio-inspired optimization algorithms. It attempts to imitate a single bacterium or groups of E. Coli bacteria. In BF algorithm, a set of bacteria forages towards a nutrient rich medium to get more nutrients. A new edge detection technique is proposed to deal with the noisy image using fuzzy derivative and bacterial foraging algorithm. The bacteria detect edge pixels as well as noisy pixels in its path during the foraging. The new fuzzy inference rules are devised and the direction of movement of each bacterium is found using these rules. During the foraging if a bacterium encounters a noisy pixel, it first removes the noisy pixel using an adaptive fuzzy switching median filter in Toh and Isa (IEEE Signal Process Lett 17(3):281---284, 2010). If the bacterium does not encounter any noisy pixel then it searches only the edge pixel in the image and draws the edge map. This approach can detect the edges in an image in the presence of impulse noise up to 30%.

Performance Evaluation of Fuzzy Switching Position Controller for Automation and Process Industry Control

This paper presents the development and implementation of a new and practical fuzzy switching position controller for process industry control. Compatible with the structure of a bang-bang controller, a two-input fuzzy switching position controller with five rules is proposed. The idea is based on constructing a fuzzy switching control law which is functionally analogous to a traditional bang-bang controller. Fuzzy logic rules are used to enable an improved version of the conventional bang-bang control. The objective is to replace the conventional bang-bang controller with the proposed fuzzy switching position controller. A modified three-stage bang-bang controller is also designed and implemented. This controller is tested under the same conditions as that of the proposed fuzzy switching position controller, and its performance is used as a basis of comparison by which both controllers are measured. Both controllers are implemented in real time, using the position control of a brushless dc motor drive found in the majority of these new robotic devices. A test bed was built and tested in the laboratory using a collection of tools that include both off-the-shelf hardware (dSPACE DSP DS1104) and software (MATLAB/Simulink). Experimental results show that the fuzzy switching position controller produces adequate control performance, particularly in handling nonlinearities and external disturbances. The efficacy of the fuzzy switching position controller is demonstrated by its positive results, practicality, and feasibility in the process industry sector, when compared with a modified traditional bang-bang controller.

Research of Subsection Optimal Acceleration Control Law of Aircraft Engines Using Fuzzy Switching Based on a Cloud Model

Considering the problem of optimal acceleration control of aircraft engine, the subsection optimal acceleration control method using fuzzy switching based on a cloud model is proposed. The acceleration process is divided into three phases, optimal objective functions and constraints are different in each phase. The optimal acceleration control law is obtained using the SQP algorithm with adjusted objective functions and constraints to improve the ability of engine acceleration. Considering the significant jump of control parameters and the exceeding limit of aircraft engine state parameters problem during the shift of different phase on the acceleration process, the method of fuzzy switching controller based on a cloud model is designed. Compared with the normal SQP acceleration control law and the subsection one based on direct switching, the simulation results show that the proposed method not only is the most effective control law but also can ensure the aircraft engine stable.

An Automation Fuzzy Switching Bang-Bang Controller for Industrial Applications

In this paper, a fuzzy switching bang-bang controller-based theoretical bang-bang was designed and implemented in the laboratory and its effectiveness in tracking application has been verified. The key advantage is the ease of the design and flexibility. The ability of the fuzzy bang-bang controller to achieve the tracking process with a high degree of accuracy, even in the presence of external disturbance was illustrated. The sensitivity of the proposed controller to rapid load changes in the external load was verified, and very promising results were observed. Experimental results confirmed the effectiveness of the proposed controller in position tracking and robustness to system parameter variations. The fuzzy switching bang-bang controller showed definite promise for this application due to the ability to combine many control laws together in a simple, intuitive manner.

A data fusion framework with novel hybrid algorithm for multi-agent Decision Support System for Forest Fire

In this study Forest Fire Decision Support System (FOFDESS) which is a multi-agent Decision Support System for Forest Fire has been presented. Depending on the existing meteorological state and environmental observations, FOFDESS does the fire danger rating by predicting the forest fire and it can also approximate fire spread speed and quickly detect a started fire. Some data fusion algorithms such as Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), Fuzzy Switching (FS) and image processing have been used for these operations in FOFDESS. These algorithms have been brought together by a designed data fusion framework and a novel hybrid algorithm called NABNEF (Naive Bayes Aided Neural-Fuzzy Algorithm) has been improved for fire danger rating in FOFDESS. In this state, FOFDESS is an integrated system which includes the dimensions of prediction, detection and management. As a result of the experiments, it was found out that FOFDESS helped determining the most accurate strategy for fire fighting by producing effective results.

An Intelligent Data Analysis-Base: Evaluation of Nuclear Power Plants Output Flow

Abstract in order to realize stable electricity generation, nuclear power plant (NPP) generators are evaluated in their performance of generated output power in term of quality and quantity. Therefore, the evaluation is realized on the basis of several influence factor, which have to be analyzed via the exploitation of heterogeneous data sets obtained from scattered locations and different types of sources. In this paper, we stress the pivotal role of extended fuzzy switching regression analysis in handling this type of data, which come from real world of the NPPs industry. The key objective of this study is to implement the enhancement of a convex hull approach in the fuzzy switching regression analysis process which can be viewed as an intelligent data analysis (IDA) approach. This approach is concerned with the effective combination of fuzzy sets theory with the analysis of large amounts of online data. For deploying the multisource data problem, the fuzzy regression analysis based on convex-hull, specifically Beneath-Beyond algorithm. The selected IDA becomes a potential analysis vehicle to successfully reduce the computing time as well as minimize the computational complexity. It is shown that the proposed approach becomes an efficient vehicle for the evaluation of produced output flow by NPPs. The study offers an interesting and practically appealing alterative platform to evaluate the quality and quantity of produced output flow of NPPs.

Cluster-based adaptive fuzzy switching median filter for universal impulse noise reduction

In this paper, we present a novel method for the removal of impulse noise from digital images. The proposed filter, called the Cluster-based Adaptive Fuzzy Switching Median (CAFSM), is composed of a cascaded easyto- implement impulse detector and a detail preserving noise filter. Initially, the impulse detector classifies any possible impulsive noise pixels. Subsequently, the filtering phase replaces the detected noise pixels. In addition, the filtering phase employs fuzzy reasoning to deal with uncertainties present in local information. Contrary to many existing filters that only focus on a particular impulse noise model, the CAFSM filter is capable of filtering all kinds of impulse noise -the random-valued and/or fixed-valued impulse noise models. Extensive simulations conducted on 100 monochrome images under a wide range of noise densities show that the CAFSM filter substantially outperforms other state-of-the-art impulse noise filters. Furthermore, the relatively fast processing time suggests the CAFSM filter’s applicability in consumer electronic products such as digital cameras <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Genetic Fuzzy Filter Based on MAD and ROAD to Remove Mixed Impulse Noise

In this paper, we propose a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD). The proposed method consists of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization using genetic algorithms (GA) to perform efficient and effective noise removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a pixel. Fuzzy inference system is used to justify the degree of which a pixel can be categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme that adopts median filter as the main estimator is applied to the filtering. The GA training aims to find the best parameters for the fuzzy sets in the fuzzy noise detection. By the experimental results, the proposed method has successfully removed mixed impulse noise in low to medium probabilities, while keeping the uncorrupted pixels less affected by the median filtering. It also surpasses the other methods, either classical or soft computing-based approaches to impulse noise removal, in MAE and PSNR evaluations.

Multiple fuzzy neural networks modeling with sparse data

It is difficult to establish a black-box model for sparse data, because not enough data can be applied for training. This paper presents a novel identification approach using multiple fuzzy neural networks. It focuses on structure and parameters uncertainty which have been widely explored in the literature. Firstly, the sparse data are used within a fixed time interval to generate model structure. Then kernel regression methods are used to generate training data, a stable updating algorithm is proposed to train the membership functions. To cope structure change, a hysteresis strategy is proposed to enable multiple fuzzy neural identifier switching with guaranteed performance. Both theoretic analysis and simulation example show the efficacy of the proposed method.

Application of multi-model control with fuzzy switching to a micro hydro-electrical power plant

Modelling hydraulic turbine generating systems is not an easy task because they are non-linear and uncertain where the operating points are time varying. One way to overcome this problem is to use Takagi–Sugeno (TS) models, which offer the possibility to apply some tools from linear control theory, whereas those models are composed of linear models connected by a fuzzy activation function. This paper presents an approach to model and control a micro hydro power plant considered as a non-linear system using TS fuzzy systems. A TS fuzzy system with local models is used to obtain a global model of the studied plant. Then, to combine efficiency and simplicity of design, PI controllers are synthesised for each considered operating point to be used as conclusion of an electrical load TS Fuzzy controller. The latter ensures the global stability and desired performance despite the change of operating point. The proposed approach (model and controller) is tested on a laboratory prototype, where the obtained results show their efficiency and their capability to ensure good performance despite the non-linear nature of the plant.

Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This letter presents a novel two-stage noise adaptive fuzzy switching median (NAFSM) filter for salt-and-pepper noise detection and removal. Initially, the detection stage will utilize the histogram of the corrupted image to identify noise pixels. These detected “noise pixels” will then be subjected to the second stage of the filtering action, while “noise-free pixels” are retained and left unprocessed. Then, the NAFSM filtering mechanism employs fuzzy reasoning to handle uncertainty present in the extracted local information as introduced by noise. Simulation results indicate that the NAFSM is able to outperform some of the salt-and-pepper noise filters existing in literature. </para>

The Assessment Method of Supply Chain Risks Based on Vague Sets

In order to evaluate the supply chain risks, proposed the combination method based on Vague Sets and fuzzy risk assessment, and took an application model for example. First introduced the basic knowledge about fuzzy mathematics and Vague sets. Then gave the model of risk assessment and its algorithms, determined the major risk factors using Vague set theory and fuzzy mathematics method, combined the expert evaluation and fuzzy switch, applied the fuzzy analysis method to the static risk assessment of suppliers’ risks and the individual risk status assessment of special event. Finally, described the specific flow by an example.

Fuzzy Switching Technique Applied to PWM Boost Converter Operating in Mixed Conduction Mode for PV Systems

Due to the variation of the maximum power point (MPP) of photovoltaic (PV) generators with solar radiation and temperature, boost DC-DC converters placed between PV modules and inverters in grid-connected PV systems have to be controlled in a variable operating-point condition. In addition, inductor current dynamics changes suddenly when moving from continuous to discontinuous conduction mode. The previous difficulties make the design of reliable and fast control laws for the input voltage of boost converters complicated. The aim of this paper is to propose a control algorithm based on cascaded-loop control. The input voltage is controlled by the outer loop. The inductor current is controlled by an inner loop strategy which is able to perform in mixed conduction mode, owing to the fuzzy switching technique. Simulation and experimental results for a 10-kW boost converter show that the proposed strategy achieves an accurate and robust performance at every operating point, even if the inductor value varies in a wide range; thus, fast MPP tracking techniques can be implemented. An additional advantage is that constant switching frequency is achieved.

Fuzzy-Logic-Based Sliding-Mode Controller Design for Position-Sensorless Electric Vehicle

A control system by means of the back electromotive force method is designed for the position-sensorless electric vehicle (EV) after analyzing the principle of position-sensorless control for brushless dc motor. Aiming at the main problems of EV: variation of model and short driving range, a fuzzy sliding-mode controller (FSMC) with regeneration is designed for the EV. The controller uses fuzzy switching gain adjustment to avoid the whippings. The experimental results show that the control system of position-sensorless EV can successfully achieve position-sensorless commutation control and replace Hall sensors. FSMC possesses better performance and higher efficiency than the proportional-integral-differential controller. Additionally, it can recover more energy and increase more driving range.

Adaptive Fuzzy Switched Swing-Up and Sliding Control for the Double-Pendulum-and-Cart System

In this paper, an adaptive fuzzy switched swing-up and sliding controller (AFSSSC) is proposed for the swing-up and position controls of a double-pendulum-and-cart system. The proposed AFSSSC consists of a fuzzy switching controller (FSC), an adaptive fuzzy swing-up controller (FSUC), and an adaptive hybrid fuzzy sliding controller (HFSC). To simplify the design of the adaptive HFSC, the double-pendulum-and-cart system is reformulated as a double-pendulum and a cart subsystem with matched time-varying uncertainties. In addition, an adaptive mechanism is provided to learn the parameters of the output fuzzy sets for the adaptive HFSC. The FSC is designed to smoothly switch between the adaptive FSUC and the adaptive HFSC. Moreover, the sliding mode and the stability of the fuzzy sliding control systems are guaranteed. Simulation results are included to illustrate the effectiveness of the proposed AFSSSC.