Showing papers on "Fuzzy control system published in 2007"

Fuzzy Systems Engineering: Toward Human-Centric Computing

Abstract: A self-contained treatment of fuzzy systems engineering, offering conceptual fundamentals, design methodologies, development guidelines, and carefully selected illustrative material Forty years have passed since the birth of fuzzy sets, in which time a wealth of theoretical developments, conceptual pursuits, algorithmic environments, and other applications have emerged. Now, this reader-friendly book presents an up-to-date approach to fuzzy systems engineering, covering concepts, design methodologies, and algorithms coupled with interpretation, analysis, and underlying engineering knowledge. The result is a holistic view of fuzzy sets as a fundamental component of computational intelligence and human-centric systems. Throughout the book, the authors emphasize the direct applicability and limitations of the concepts being discussed, and historical and bibliographical notes are included in each chapter to help readers view the developments of fuzzy sets from a broader perspective. A radical departure from current books on the subject, Fuzzy Systems Engineering presents fuzzy sets as an enabling technology whose impact, contributions, and methodology stretch far beyond any specific discipline, making it applicable to researchers and practitioners in engineering, computer science, business, medicine, bioinformatics, and computational biology. Additionally, three appendices and classroom-ready electronic resources make it an ideal textbook for advanced undergraduate- and graduate-level courses in engineering and science.

Type-2 FLCs: A New Generation of Fuzzy Controllers

Abstract: Type-1 fuzzy logic controllers (FLCs) have been applied to date with great success to many different applications. However, for dynamic unstructured environments and many real-world applications, there is a need to cope with large amounts of uncertainties. The traditional type-1 FLC using crisp type-1 fuzzy sets cannot directly handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better performance. Hence, type-2 FLCs will have the potential to overcome the limitations of type-1 FLCs and produce a new generation of fuzzy controllers with improved performance for many applications, which require handling high levels of uncertainty. This paper introduces briefly the interval type-2 FLC and its benefits. We also present briefly the type-2 FLC application to three challenging domains: industrial control, mobile robots control and ambient intelligent environments control

A Descriptor System Approach to Fuzzy Control System Design via Fuzzy Lyapunov Functions

Abstract: There has been a flurry of research activities in the analysis and design of fuzzy control systems based on linear matrix inequalities (LMIs). This paper presents a descriptor system approach to fuzzy control system design using fuzzy Lyapunov functions. The design conditions are still cast in terms of LMIs but the proposed approach takes advantage of the redundancy of descriptor systems to reduce the number of LMI conditions which leads to less computational requirement. To obtain relaxed LMI conditions, new types of fuzzy controller and fuzzy Lyapunov function are proposed. A salient feature of the LMI conditions derived in this paper is to relate the feasibility of the LMIs to the switching speed of each linear subsystem (to be exact, to the lower bounds of time derivatives of membership functions). To illustrate the validity and applicability of the proposed approach, two design examples are provided. The first example shows that the LMI conditions based on the fuzzy Lyapunov function are less conservative than those based on a common (standard) Lyapunov function. The second example illustrates the utility of the fuzzy Lyapunov function approach in comparison with a piecewise Lyapunov function approach.

Fuzzy Multi-Level Security: An Experiment on Quantified Risk-Adaptive Access Control

Abstract: This paper presents a new model for, or rather a new way of thinking about adaptive, risk-based access control. Our basic premise is that there is always inherent uncertainty and risk in access control decisions that is best addressed in an explicit way. We illustrate this concept by showing how the rationale of the well-known, Bell-Lapadula model based, multi-level security (MLS) access control model could be used to develop a risk-adaptive access control model. This new model is more like a fuzzy logic control system than a traditional access control system and hence the name "fuzzy MLS". The long version of this paper is published as an IBM Research Report.

Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions (Mendel, J.M.; 2001) [book review]

Abstract: The book comprises 14 chapters and three appendices. The chapters are organized onto four parts: Preliminaries, Type-1 Fuzzy Logic Systems, Type-2 Fuzzy sets, and Type-2 Fuzzy Logic Systems. The book proves to be a valuable resource for professionals seeking to work with fuzzy sets in general and type-2 fuzzy sets in particular.

Foundations of fuzzy control

Abstract: Foundations of Fuzzy Control , Foundations of Fuzzy Control , کتابخانه الکترونیک و دیجیتال - آذرسا

Multiobjective Genetic Clustering for Pixel Classification in Remote Sensing Imagery

Abstract: An important approach for unsupervised landcover classification in remote sensing images is the clustering of pixels in the spectral domain into several fuzzy partitions. In this paper, a multiobjective optimization algorithm is utilized to tackle the problem of fuzzy partitioning where a number of fuzzy cluster validity indexes are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements. Real-coded encoding of the cluster centers is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Different landcover regions in remote sensing imagery have also been classified using the proposed technique to establish its efficiency

Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

Abstract: Research into adjusting the probabilities of crossover and mutation pm in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. px and pm greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of px and pm , this paper presents the use of fuzzy logic to adaptively adjust the values of px and pm in GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of px and pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA using fixed values of px and pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions

Adaptive Fuzzy Output Tracking Control of MIMO Nonlinear Uncertain Systems

Abstract: In this paper, the adaptive fuzzy tracking control problem is discussed for a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems with the block-triangular structure The fuzzy logic systems are used to approximate the unknown nonlinear functions By using the backstepping technique, the adaptive fuzzy tracking control design scheme is developed, which has minimal learning parameterizations The adaptive fuzzy tracking controllers guarantee that the outputs of systems converge to a small neighborhood of the reference signals and all the signals in the closed-loop system are semiglobally uniformly ultimately bounded Two examples are used to show the effectiveness of the approach

Stabilization of Nonlinear Systems Under Variable Sampling: A Fuzzy Control Approach

Abstract: This paper investigates the problem of stabilization for a Takagi-Sugeno (T-S) fuzzy system with nonuniform uncertain sampling. The sampling is not required to be periodic, and the only assumption is that the distance between any two consecutive sampling instants is less than a given bound. By using the input delay approach, the T-S fuzzy system with variable uncertain sampling is transformed into a continuous-time T-S fuzzy system with a delay in the state. Though the resulting closed-loop state-delayed T-S fuzzy system takes a standard form, the existing results on delay T-S fuzzy systems cannot be used for our purpose due to their restrictive assumptions on the derivative of state delay. A new condition guaranteeing asymptotic stability of the closed-loop sampled-data system is derived by a Lyapunov approach plus the free weighting matrix technique. Based on this stability condition, two procedures for designing state-feedback control laws are given: one casts the controller design into a convex optimization by introducing some over design and the other utilizes the cone complementarity linearization idea to cast the controller design into a sequential minimization problem subject to linear matrix inequality constraints, which can be readily solved using standard numerical software. An illustrative example is provided to show the applicability and effectiveness of the proposed controller design methodology.

Fault Detection for Uncertain Fuzzy Systems: An LMI Approach

Abstract: This paper studies the problem of designing a robust fault-detection system for uncertain Takagi-Sugeno fuzzy models. The worst case fault sensitivity measure is formulated in terms of linear matrix inequalities. The existence of a robust fault detection system that guarantees i) the L2-gain from a fault signal to a residual signal greater than a prescribed value and ii) the L2-gain from an exogenous input to a residual signal less than a prescribed value is given in terms of the solvability of linear matrix inequalities. Numerical examples are used to illustrate the effectiveness of the proposed design techniques.

New Approach to Delay-Dependent Stability Analysis and Stabilization for Continuous-Time Fuzzy Systems With Time-Varying Delay

Abstract: This paper is concerned with delay-dependent stability analysis and stabilization problems for continuous-time Takagi and Sugeno (T-S) fuzzy systems with a time-varying delay A new method for the delay-dependent stability analysis and stabilization is suggested, which is less conservative than other existing ones First, based on a fuzzy Lyapunov-Krasovskii functional (LKF), a delay-dependent stability criterion is derived for the open-loop fuzzy systems In the derivation process, some free fuzzy weighting matrices are introduced to express the relationships among the terms of the system equation, and among the terms in the Leibniz-Newton formula Then, a delay-dependent stabilization condition based on the so-called parallel distributed compensation (PDC) scheme is worked out for the closed-loop fuzzy systems The proposed stability criterion and stabilization condition are represented in terms of linear matrix inequalities (LMIs) and compared with the existing ones via two examples Finally, application to control of a truck-trailer is also given to illustrate the effectiveness of the proposed design method

T-S Fuzzy-Model-Based Robust $H_{\infty}$ Design for Networked Control Systems With Uncertainties

Abstract: This paper concerns a problem of robust Hinfin control for a class of uncertain nonlinear networked control systems (NCSs), which can be represented by a T-S fuzzy model with uncertainties. Both network-induced delay and packet dropout are addressed. The controller design method is presented based on a delay-dependent approach, and the robust Hinfin controller gain matrices are obtain by solving a set of linear matrix inequalities (LMIs). Moreover, a general Lyapunov-Krasovskii functional is used, and some slack matrices, which bring much flexibility in solving LMIs, are introduced during the proof. Simulation results show the validity of the proposed method.

FPGA-Based Speed Control IC for PMSM Drive With Adaptive Fuzzy Control

Abstract: The new generation of field programmable gate array (FPGA) technologies enables an embedded processor intellectual property (IP) and an application IP to be integrated into a system-on-a-programmable-chip (SoPC) developing environment. Therefore, this study presents a speed control integrated circuit (IC) for permanent magnet synchronous motor (PMSM) drive under this SoPC environment. First, the mathematic model of PMSM is defined and the vector control used in the current loop of PMSM drive is explained. Then, an adaptive fuzzy controller adopted to cope with the dynamic uncertainty and external load effect in the speed loop of PMSM drive is proposed. After that, an FPGA-based speed control IC is designed to realize the controllers. The proposed speed control IC has two IPs, a Nios II embedded processor IP and an application IP. The Nios II processor is used to develop the adaptive fuzzy controller in software due to the complicated control algorithm and low sampling frequency control (speed control: 2 kHz). The designed application IP is utilized to implement the current vector controller in hardware owing to the requirement for high sampling frequency control (current loop: 16 kHz, pulsewidth modulation circuit: 4-8 MHz) but simple computation. Finally, an experimental system is set up and some experimental results are demonstrated.

Architecture Complexity and Energy Efficiency of Small Wind Turbines

Abstract: The power characteristics of wind turbines are nonlinear. It is particularly true for vertical-axis turbines whose provided power is very sensitive to the load. Thus, controlling the operating point is essential to optimize the energetic behavior. Several control strategies (maximum power point tracking) can be used for the energy conversion. If the wind-turbine characteristic Cp(lambda) is supposed to be a priori known, it can be used for optimal control of the torque, speed, or system output power. On the contrary, if this characteristic is unknown, an operational seeking algorithm such as fuzzy logic has to be implemented. Several structures with different associated complexity degrees can be used, in particular, the structure of the ac-dc conversion, which can be either a pulsewidth-modulation voltage-source rectifier or a simple diode bridge. A comparative study of the corresponding control strategies and architectures is proposed in this paper regarding the tradeoffs between structure complexity and energy efficiency. The analysis is based on simulations and experiments

Guaranteed Cost Networked Control for T–S Fuzzy Systems With Time Delays

Abstract: This paper develops a guaranteed cost networked control (GCNC) method for Takagi-Sugeno (T-S) fuzzy systems with time delays. The state feedback controller is designed via the networked control system (NCS) theory. The stability of the overall fuzzy system using GCNC is also established. Network-induced delay in network transmission and packet dropout are analyzed. Some deductions are also extended to uncertain systems. Simulation results show the validity of the present control scheme

A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking

Abstract: One important problem in autonomous robot navigation is the effective following of an unknown path traced in the environment in compliance with the kinematic limits of the vehicle, i.e., bounded linear and angular velocities and accelerations. In this case, the motion planning must be implemented in real-time and must be robust with respect to the geometric characteristics of the unknown path, namely curvature and sharpness. To achieve good tracking capability, this paper proposes a path following approach based on a fuzzy-logic set of rules which emulates the human driving behavior. The input to the fuzzy system is represented by approximate information concerning the next bend ahead the vehicle; the corresponding output is the cruise velocity that the vehicle needs to attain in order to safely drive on the path. To validate the proposed algorithm two completely different experiments have been run: in the first experiment, the vehicle has to perform a lane-following task acquiring lane information in real-time using an onboard camera; in the second, the motion of the vehicle is obtained assigning in real-time a given time law. The obtained results show the effectiveness of the proposed method

Fuzzy Sliding-Mode Control Using Adaptive Tuning Technique

Abstract: This study mainly deals with the key problem of chattering phenomena on the conventional sliding-mode control (SMC) and investigates an adaptive fuzzy sliding-mode control (AFSMC) system for an indirect field-oriented induction motor (IM) drive to track periodic commands. First, an indirect field-orientation method for an IM drive is introduced briefly. Moreover, a fuzzy logic inference mechanism is utilized for implementing a fuzzy hitting control law to remove completely the chattering phenomena on the conventional SMC. In addition, to confront the uncertainties existed in practical applications, an adaptive algorithm, which is derived in the sense of Lyapunov stability theorem, is utilized to adjust the fuzzy parameter for further assuring robust and optimal control performance. The indirect field-oriented IM drive with the AFSMC scheme possesses the salient advantages of simple control framework, free from chattering, stable tracking control performance, and robust to uncertainties. Furthermore, numerical simulation and experimental results due to periodic sinusoidal commands are provided to verify the effectiveness of the proposed control strategy, and its advantages are indicated in comparison with the conventional SMC system and the SMC system with a boundary layer

A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection

Abstract: Linguistic fuzzy modeling allows us to deal with the modeling of systems by building a linguistic model which is clearly interpretable by human beings. However, since the accuracy and the interpretability of the obtained model are contradictory properties, the necessity of improving the accuracy of the linguistic model arises when complex systems are modeled. To solve this problem, one of the research lines in recent years has led to the objective of giving more accuracy to linguistic fuzzy modeling without losing the interpretability to a high level. In this paper, a new postprocessing approach is proposed to perform an evolutionary lateral tuning of membership functions, with the main aim of obtaining linguistic models with higher levels of accuracy while maintaining good interpretability. To do so, we consider a new rule representation scheme base on the linguistic 2-tuples representation model which allows the lateral variation of the involved labels. Furthermore, the cooperation of the lateral tuning together with fuzzy rule reduction mechanisms is studied in this paper, presenting results on different real applications. The obtained results show the good performance of the proposed approach in high-dimensional problems and its ability to cooperate with methods to remove unnecessary rules.

Robust Fuzzy Design for Nonlinear Uncertain Stochastic Systems via Sliding-Mode Control

Abstract: This paper deals with the sliding-mode control (SMC) problem for nonlinear stochastic time-delay systems by means of fuzzy approach. The Takagi-Sugeno (T-S) fuzzy stochastic time-delay model with parametric uncertainties and unknown nonlinearities is presented. A sufficient condition for the exponential stability in mean square of the sliding motion is also derived. Moreover, it is shown that when the linear matrix inequalities (LMIs) with equality constraint are feasible, the designs of both sliding surface and sliding-mode controller can be easily obtained via convex optimization. A simulation example illustrating the proposed method is given.

A Design Method for PI-like Fuzzy Logic Controllers for DC–DC Converter

Abstract: This paper proposes a novel design procedure of proportional and integral (Pl)-like fuzzy logic controller (FLC) for DC-DC converters that integrates linear control techniques with fuzzy logic. The design procedure allows the small signal model of the converter and linear control design techniques to be used in the initial stages of FLC design. This simplifies the small signal design and the stability assessment of the FLC. By exploiting the fuzzy logic structure of the controller, heuristic knowledge is incorporated in the design, which results in a nonlinear controller with improved performance over linear PI controllers.