Showing papers on "Fuzzy control system published in 2008"

Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach

Abstract: From the Publisher: A comprehensive treatment of model-based fuzzy control systems This volume offers full coverage of the systematic framework for the stability and design of nonlinear fuzzy control systems. Building on the Takagi-Sugeno fuzzy model, authors Tanaka and Wang address a number of important issues in fuzzy control systems, including stability analysis, systematic design procedures, incorporation of performance specifications, numerical implementations, and practical applications. Issues that have not been fully treated in existing texts, such as stability analysis, systematic design, and performance analysis, are crucial to the validity and applicability of fuzzy control methodology. Fuzzy Control Systems Design and Analysis addresses these issues in the framework of parallel distributed compensation, a controller structure devised in accordance with the fuzzy model. This balanced treatment features an overview of fuzzy control, modeling, and stability analysis, as well as a section on the use of linear matrix inequalities (LMI) as an approach to fuzzy design and control. It also covers advanced topics in model-based fuzzy control systems, including modeling and control of chaotic systems. Later sections offer practical examples in the form of detailed theoretical and experimental studies of fuzzy control in robotic systems and a discussion of future directions in the field. Fuzzy Control Systems Design and Analysis offers an advanced treatment of fuzzy control that makes a useful reference for researchers and a reliable text for advanced graduate students in the field.

Computational Intelligence: An Introduction

Abstract: The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

DSC Approach to Robust Adaptive Fuzzy Tracking Control for Strict-Feedback Nonlinear Systems

Abstract: A robust adaptive tracking control approach is presented for a class of strict-feedback single-input-single-output nonlinear systems. By employing radial-basis-function neural networks to account for system uncertainties, the proposed scheme is developed by combining ?dynamic surface control? and ?minimal learning parameter? techniques. The key features of the algorithm are that, first, the problem of ?explosion of complexity? inherent in the conventional backstepping method is avoided, second, the number of parameters updated online for each subsystem is reduced to 2, and, third, the possible controller singularity problem in the approximation-based adaptive control schemes with feedback linearization technique is removed. These features result in a much simpler adaptive control algorithm, which is convenient to implement in applications. In addition, it is shown via input-to-state stability theory and small gain approach that all signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, three simulation examples are used to demonstrate the effectiveness of the proposed scheme.

Stability Analysis of Interval Type-2 Fuzzy-Model-Based Control Systems

Abstract: This paper presents the stability analysis of interval type-2 fuzzy-model-based (FMB) control systems. To investigate the system stability, an interval type-2 Takagi-Sugeno (T-S) fuzzy model, which can be regarded as a collection of a number of type-1 T-S fuzzy models, is proposed to represent the nonlinear plant subject to parameter uncertainties. With the lower and upper membership functions, the parameter uncertainties can be effectively captured. Based on the interval type-2 T-S fuzzy model, an interval type-2 fuzzy controller is proposed to close the feedback loop. To facilitate the stability analysis, the information of the footprint of uncertainty is used to develop some membership function conditions, which allow the introduction of slack matrices to handle the parameter uncertainties in the stability analysis. Stability conditions in terms of linear matrix inequalities are derived using a Lyapunov-based approach. Simulation examples are given to illustrate the effectiveness of the proposed interval type-2 FMB control approach.

FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

Abstract: In this paper, we introduce a new algorithm for incremental learning of a specific form of Takagi-Sugeno fuzzy systems proposed by Wang and Mendel in 1992. The new data-driven online learning approach includes not only the adaptation of linear parameters appearing in the rule consequents, but also the incremental learning of premise parameters appearing in the membership functions (fuzzy sets), together with a rule learning strategy in sample mode. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The modifications include an automatic generation of new clusters based on the nature, distribution, and quality of new data and an alternative strategy for selecting the winning cluster (rule) in each incremental learning step. Antecedent and consequent learning are connected in a stable manner, meaning that a convergence toward the optimal parameter set in the least-squares sense can be achieved. An evaluation and a comparison to conventional batch methods based on static and dynamic process models are presented for high-dimensional data recorded at engine test benches and at rolling mills. For the latter, the obtained data-driven fuzzy models are even compared with an analytical physical model. Furthermore, a comparison with other evolving fuzzy systems approaches is carried out based on nonlinear dynamic system identification tasks and a three-input nonlinear function approximation example.

Energy Management Fuzzy Logic Supervisory for Electric Vehicle Power Supplies System

Abstract: This paper introduces an energy management strategy based on fuzzy logic supervisory for road electric vehicle, combining a fuel cell power source and two energy storage devices, i.e., batteries and ultracapacitors. The control strategy is designed to achieve the high-efficiency operation region of the individual power source and to regulate current and voltage at peak and average power demand, without compromising the performance and efficiency of the overall system. A multiple-input power electronic converter makes the interface among generator, energy storage devices, and the voltage dc-link bus. Classical regulators implement the control loops of each input of the converter. The supervisory system coordinates the power flows among the power sources and the load. The paper is mainly focused on the fuzzy logic supervisory for energy management of a specific power electronic converter control algorithm. Nevertheless, the proposed system can be easily adapted to other converters arrangements or to distributed generation applications. Simulation and experimental results on a 3-kW prototype prove that the fuzzy logic is a suitable energy management control strategy.

Fuzzy Rule-Based Bayesian Reasoning Approach for Prioritization of Failures in FMEA

Abstract: This paper presents a novel, efficient fuzzy rule-based Bayesian reasoning (FuRBaR) approach for prioritizing failures in failure mode and effects analysis (FMEA). The technique is specifically intended to deal with some of the drawbacks concerning the use of conventional fuzzy logic (i.e. rule-based) methods in FMEA. In the proposed approach, subjective belief degrees are assigned to the consequent part of the rules to model the incompleteness encountered in establishing the knowledge base. A Bayesian reasoning mechanism is then used to aggregate all relevant rules for assessing and prioritizing potential failure modes. A series of case studies of collision risk between a floating, production, storage, and off loading (FPSO) system and a shuttle tanker caused by technical failure during tandem off loading operation is used to illustrate the application of the proposed model. The reliability of the new approach is tested by using a benchmarking technique (with a well-established fuzzy rule-based evidential reasoning method), and a sensitivity analysis of failure priority values.

Adaptive Control of a Class of Nonlinear Pure-Feedback Systems Using Fuzzy Backstepping Approach

Abstract: A controller is proposed for the robust backstepping control of a class of nonlinear pure-feedback systems using fuzzy logic. The proposed control scheme utilizes fuzzy logic systems to learn the behavior of the unknown plant dynamics. Filtered signals are employed to circumvent algebraic loop problems encountered in the implementation of the usual controllers, and the approximation errors can be efficiently counteracted by employing smooth robust compensators. Most importantly, the uniform ultimate boundedness of all signals in the closed-loop system can be guaranteed, and a priori knowledge of the plant dynamics is no longer required. Furthermore, the proposed method can be used for adaptive control of a large class of single-input--single-output nonlinear systems in both strict-feedback and pure-feedback forms, and has great potential in many diverse applications. The performance of the proposed approach is demonstrated through three simulation examples, including one nonlinear pure-feedback and two nonlinear strict-feedback systems.

Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study

Abstract: One of the main problems for effective control of an uncertain system is the creation of the proper knowledge base for the control system. In this paper, the integration of fuzzy set theory and wavelet neural networks (WNNs) is proposed to alleviate the problem. The proposed fuzzy WNN is constructed on the base of a set of fuzzy rules. Each rule includes a wavelet function in the consequent part of the rule. The parameter update rules of the system are derived based on the gradient descent method. The structure is tested for the identification and the control of the dynamic plants commonly used in the literature. It is seen that the proposed structure results in a better performance despite its smaller parameter space.

Fuzzy State/Disturbance Observer Design for T–S Fuzzy Systems With Application to Sensor Fault Estimation

Abstract: A novel fuzzy-observer-design approach is presented for Takagi-Sugeno fuzzy models with unknown output disturbances. In order to decouple the unknown output disturbance, an augmented fuzzy descriptor model is constructed by supposing the disturbance to be an auxiliary state vector. A fuzzy state-space observer is next designed for the augmented fuzzy descriptor system, and the simultaneous estimates of the original state and disturbance are thus obtained. The proposed observer technique is further applied to estimate sensor faults. Finally, a numerical example is given to illustrate the design procedure, and the simulation results show the desired tracking performance. The pre knowledge of the disturbance and fault is not necessary for our design. Moreover, the considered disturbance and sensor fault can be in any form.

Pitch angle control for variable speed wind turbines

Abstract: Pitch angle control is the most common means for adjusting the aerodynamic torque of the wind turbine when wind speed is above rated speed and various controlling variables may be chosen, such as wind speed, generator speed and generator power. As conventional pitch control usually use PI controller, the mathematical model of the system should be known well. A fuzzy logic pitch angle controller is developed in this paper, in which it does not need well known about the system and the mean wind speed is used to compensate the non-linear sensitivity. The fuzzy logic control strategy may have the potential when the system contains strong non-linearity, such as wind turbulence is strong, or the control objectives include fatigue loads. The design of the fuzzy logic controller and the comparisons with conversional pitch angle control strategies with various controlling variables are carried out. The simulation shows that the fuzzy logic controller can achieve better control performances than conventional pitch angle control strategies, namely lower fatigue loads, lower power peak and lower torque peak.

State of the Art in Vehicle Active Suspension Adaptive Control Systems Based on Intelligent Methodologies

Abstract: This paper reviews computational-intelligence-involved approaches in active vehicle suspension control systems with a focus on the problems raised in practical implementations by their nonlinear and uncertain properties. After a brief introduction on active suspension models, the paper explores the state of the art in fuzzy inference systems, neural networks, genetic algorithms, and their combination for suspension control issues. Discussions and comments are provided based on the reviewed simulation and experimental results. The paper is concluded with remarks and future directions.

Fractional Fuzzy Adaptive Sliding-Mode Control of a 2-DOF Direct-Drive Robot Arm

Abstract: This paper presents a novel parameter adjustment scheme to improve the robustness of fuzzy sliding-mode control achieved by the use of an adaptive neuro-fuzzy inference system (ANFIS) architecture. The proposed scheme utilizes fractional-order integration in the parameter tuning stage. The controller parameters are tuned such that the system under control is driven toward the sliding regime in the traditional sense. After a comparison with the classical integer-order counterpart, it is seen that the control system with the proposed adaptation scheme displays better tracking performance, and a very high degree of robustness and insensitivity to disturbances are observed. The claims are justified through some simulations utilizing the dynamic model of a 2-DOF direct-drive robot arm. Overall, the contribution of this paper is to demonstrate that the response of the system under control is significantly better for the fractional-order integration exploited in the parameter adaptation stage than that for the classical integer-order integration.

A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming

Abstract: Elevator group supervisory control systems (EGSCSs) are designed so that the movement of several elevators in a building is controlled efficiently. The efficient control of EGSCSs using conventional control methods is very difficult due to its complexity, so it is becoming popular to introduce artificial intelligence (AI) technologies into EGSCSs in recent years. As a new approach, a graph-based evolutionary method named genetic network programming (GNP) has been applied to the EGSCSs, and its effectiveness is clarified. The GNP can introduce various a priori knowledge of the EGSCSs in its node functions easily, and can execute an efficient rule-based group supervisory control that is optimized in an evolutionary way. Meanwhile, double-deck elevator systems (DDESs) where two cages are connected in a shaft have been developed for the rising demand of more efficient transport of passengers in high-rise buildings. The DDESs have specific features due to the connection of cages and the need for comfortable riding; so its group supervisory control becomes more complex and requires more efficient group control systems than the conventional single-deck elevator systems (SDESs). In this paper, a new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations. First, optimization of the GNP for DDSEs is executed. Second, the performance of the proposed method is evaluated by comparison with conventional methods, and the obtained control rules in GNP are studied. Finally, the reduction of space requirements compared with SDESs is confirmed.

Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction

Abstract: The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms, and recursive least-squares optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance, and learning capability.

Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling

Abstract: A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

Delay-Dependent Guaranteed Cost Control for Uncertain Stochastic Fuzzy Systems With Multiple Time Delays

Abstract: This paper studies the guaranteed cost control problem for a class of uncertain stochastic nonlinear systems with multiple time delays represented by the Takagi-Sugeno fuzzy model with uncertain parameters. By constructing a new stochastic Lyapunov-Krasovskii functional, sufficient conditions for delay-dependent guaranteed cost control are obtained which do not require system transformation or relaxation matrices. Conditions for the existence of an optimal guaranteed cost controller are presented in the linear matrix inequality format. Simulation examples are provided to demonstrate the effectiveness of the proposed approach in this paper.