Showing papers on "Fuzzy control system published in 2001"

Modern Control Engineering

Abstract: Introduction to automatic control systems mathematical background mathematical models of systems classical time-domain analysis of control systems state-space analysis of control systems stability the root locus method frequency domain analysis classical control design methods state-space design methods optimal control digital control system identification adaptive control robust control fuzzy control. Appendices: Laplace transform tables the Z-transform transform tables.

Fuzzy adaptive particle swarm optimization

Abstract: A fuzzy system is implemented to dynamically adapt the inertia weight of the particle swarm optimization algorithm (PSO). Three benchmark functions with asymmetric initial range settings are selected as the test functions. The same fuzzy system has been applied to all three test functions with different dimensions. The experimental results illustrate that the fuzzy adaptive PSO is a promising optimization method, which is especially useful for optimization problems with a dynamic environment.

Parameterized linear matrix inequality techniques in fuzzy control system design

Abstract: This paper proposes different parameterized linear matrix inequality (PLMI) characterizations for fuzzy control systems. These PLMI characterizations are, in turn, relaxed into pure LMI programs, which provides tractable and effective techniques for the design of suboptimal fuzzy control systems. The advantages of the proposed methods over earlier ones are then discussed and illustrated through numerical examples and simulations.

Designing fuzzy inference systems from data: An interpretability-oriented review

Abstract: Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined.

Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model

Abstract: This study introduces a fuzzy control design method for nonlinear systems with a guaranteed H/sub /spl infin// model reference tracking performance. First, the Takagi and Sugeno (TS) fuzzy model is employed to represent a nonlinear system. Next, based on the fuzzy model, a fuzzy observer-based fuzzy controller is developed to reduce the tracking error as small as possible for all bounded reference inputs. The advantage of proposed tracking control design is that only a simple fuzzy controller is used in our approach without feedback linearization technique and complicated adaptive scheme. By the proposed method, the fuzzy tracking control design problem is parameterized in terms of a linear matrix inequality problem (LMIP). The LMIP can be solved very efficiently using the convex optimization techniques. Simulation example is given to illustrate the design procedures and tracking performance of the proposed method.

Robust fuzzy control of nonlinear systems with parametric uncertainties

Abstract: Addresses the robust fuzzy control problem for nonlinear systems in the presence of parametric uncertainties. The Takagi-Sugeno (T-S) fuzzy model is adopted for fuzzy modeling of the nonlinear system. Two cases of the T-S fuzzy system with parametric uncertainties, both continuous-time and discrete-time cases are considered. In both continuous-time and discrete-time cases, sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability, for the T-S fuzzy system with parametric uncertainties. The sufficient conditions are formulated in the format of linear matrix inequalities. The T-S fuzzy model of the chaotic Lorenz system, which has complex nonlinearity, is developed as a test bed. The effectiveness of the proposed controller design methodology is finally demonstrated through numerical simulations on the chaotic Lorenz system.

Fuzzy Modeling and Control

Abstract: In the last ten years, a true explosion of investigations into fuzzy modeling and its applications in control, diagnostics, decision making, optimization, pattern recognition, robotics, etc. has been observed. The attraction of fuzzy modeling results from its intelligibility and the high effectiveness of the models obtained. Owing to this the modeling can be applied for the solution of problems which could not be solved till now with any known conventional methods. The book provides the reader with an advanced introduction to the problems of fuzzy modeling and to one of its most important applications: fuzzy control. It is based on the latest and most significant knowledge of the subject and can be used not only by control specialists but also by specialists working in any field requiring plant modeling, process modeling, and systems modeling, e.g. economics, business, medicine, agriculture,and meteorology.

Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques

Abstract: From the Publisher: A powerful, yet easy-to-use design methodology for the control of nonlinear dynamic systems A key issue in the design of control systems is proving that the resulting closed-loop system is stable, especially in cases of high consequence applications, where process variations or failure could result in unacceptable risk. Adaptive control techniques provide a proven methodology for designing stable controllers for systems that may possess a large amount of uncertainty. At the same time, the benefits of neural networks and fuzzy systems are generating much excitement-and impressive innovations-in almost every engineering discipline. Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques brings together these two different but equally useful approaches to the control of nonlinear systems in order to provide students and practitioners with the background necessary to understand and contribute to this emerging field. The text presents a control methodology that may be verified with mathematical rigor while possessing the flexibility and ease of implementation associated with "intelligent control" approaches. The authors show how these methodologies may be applied to many real-world systems including motor control, aircraft control, industrial automation, and many other challenging nonlinear systems. They provide explicit guidelines to make the design and application of the various techniques a practical and painless process. Design techniques are presented for nonlinear multi-input multi-output (MIMO) systems in state-feedback, output-feedback, continuous or discrete-time, or even decentralized form. To help students and practitioners new to the field grasp and sustain mastery of the material, the book features: Background material on fuzzy systems and neural networksStep-by-step controller designNumerous examplesCase studies using "real world" applicationsHomework problems and design projects

An optimal fuzzy PID controller

Abstract: This paper introduces an optimal fuzzy proportional-integral-derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any fuzzy rule base, and is actually a conventional PID controller with analytical formulae. The main improvement is in endowing the classical controller with a certain adaptive control capability. The constant PID control gains are optimized by using the multiobjective genetic algorithm (MOGA), thereby yielding an optimal fuzzy PID controller. Computer simulations are shown to demonstrate its improvement over the fuzzy PID controller without MOGA optimization.

Model construction, rule reduction, and robust compensation for generalized form of Takagi-Sugeno fuzzy systems

Abstract: This paper presents a systematic procedure of fuzzy control system design that consists of fuzzy model construction, rule reduction, and robust compensation for nonlinear systems. The model construction part replaces the nonlinear dynamics of a system with a generalized form of Takagi-Sugeno fuzzy systems, which is newly developed by us. The generalized form has a decomposed structure for each element of A/sub i/ and B/sub i/ matrices in consequent parts. The key feature of this structure is that it is suitable for constructing IF-THEN rules and reducing the number of IF-THEN rules. The rule reduction part provides a successive procedure to reduce the number of IF-THEN rules. Furthermore, we convert the reduction error between reduced fuzzy models and a system to model uncertainties of reduced fuzzy models. The robust compensation part achieves the decay rate controller design guaranteeing robust stability for the model uncertainties. Finally, two examples demonstrate the utility of the systematic procedure developed.

Tuning of PID controllers with fuzzy logic

Abstract: The paper presents a comparison between different methods, based on fuzzy logic, for the tuning of PID controllers. Specifically considered are different control structures in which a fuzzy mechanism is adopted to improve the performances given by Ziegler-Nichols parameters. To verify the full capabilities of each controller, genetic algorithms are used to tune the parameters of the fuzzy inference systems (scaling coefficients, shape of the membership functions, etc.). Furthermore, a discussion about the practical implementation issue of the controllers is provided, and comparisons made with a typical PID-like fuzzy controller and a standard nonlinear PID controller. The results show the superiority of the fuzzy set-point weighting methodology over the other methods.

The fusion of computationally intelligent methodologies and sliding-mode control-a survey

Abstract: This paper surveys how some "intelligence" can be incorporated in sliding-mode controllers (SMCs) by the use of computational intelligence methodologies in order to alleviate the well-known problems met in practical implementations of SMCs. The use of variable-structure system theory in design and stability analysis of fuzzy controllers is also discussed by drawing parallels between fuzzy control and SMCs. An overview of the research and applications reported in the literature in this respect is presented.

Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base

Abstract: A method is proposed to automatically learn the knowledge base by finding an appropiate data base by means of a genetic algorithm while using a simple generation method to derive the rule base. Our genetic process learns the number of linguistic terms per variable and the membership function parameters that define their semantics, while a rule base generation method learns the number of rules and their composition.

Introduction to Robotics: Analysis, Systems, Applications

Abstract: Most chapter begins with an Introduction and conclude with a Summary, References and Problems. 1. Fundamentals. What is a Robot? Classification of Robots. What is Robotics? History of Robotics. Advantages and Disadvantages of Robots. Robot Components. Robot Degrees of Freedom. Robot Joints. Robot Coordinates. Robot Reference Frames. Programming Modes. Robot Characteristics. Robot Workspace. Robot Languages. Robot Applications. Other Robots and Applications. Social Issues. 2. Robot Kinematics: Position Analysis. Robots as Mechanisms. Matrix Representation. Homogeneous Transformation Matrices. Representation of Transformations. Inverse of Transformation Matrices. Forward and Inverse Kinematics of Robots. Denavit-Hartenberg Representation of Forward Kinematic Equations of Robots. The Inverse Kinematic Solution of Robots. Inverse Kinematic Programming of Robots. Degeneracy and Dexterity. The Fundamental Problem with the Denavit-Hartenberg Representation. Design Project 1: A Three-Degree-of-Freedom Robot. 3. Differential Motions and Velocities. Differential Relationships. Jacobian. Differential Motions of a Frame. Interpretation of the Differential Change. Differential Changes Between Frames. Differential Motions of a Robot and Its Hand Frame. Calculation of the Jacobian. How to Relate the Jacobian and the Differential Operator. Inverse Jacobian. Design Project. 4. Dynamic Analysis and Forces. Lagrangian Mechanics: A Short Overview. Effective Moments of Inertia. Dynamic Equations for Multiple-Degree-of-Freedom Robots. Static Force Analysis of Robots. Transformation of Forces and Moments Between Coordinate Frames. Design Project. 5. Trajectory Planning. Path vs. Trajectory. Joint-Space vs. Cartesian-Space Descriptions. Basics of Trajectory Planning. Joint-Space Trajectory Planning. Cartesian-Space Trajectories. Continuous Trajectory Recording. Design Project. 6. Actuators. Characteristics of Actuating Systems. Comparison of Actuating Systems. Hydraulic Devices. Pneumatic Devices. Electric Motors. Microprocessor Control of Electric Motors. Magnetostrictive Actuators. Shape-Memory Type Metals. Speed Reduction. Design Project 1. Design Project 2. 7. Sensors. Sensor Characteristics. Position Sensors. Velocity Sensors. Acceleration Sensors. Force and Pressure Sensors. Torque Sensors. Microswitches. Light and Infrared Sensors. Touch and Tactile Sensors. Proximity Sensors. Range-finders. Sniff Sensors. Vision Systems. Voice Recognition Devices. Voice Synthesizers. Remote Center Compliance (RCC) Device. Design Project. 8. Image Processing and Analysis with Vision Systems. Image Processing versus Image Analysis. Two- and Three-Dimensional Image Types. What is an Image. Acquisition of Images. Digital Images. Frequency Domain vs. Spatial Domain. Fourier Transform of a Signal and its Frequency Content. Frequency Content of an Image Noise, Edges. Spatial Domain Operations: Convolution Mask. Sampling and Quantization. Sampling Theorem. Image-Processing Techniques. Histogram of Images. Thresholding. Connectivity. Noise Reduction. Edge Detection. Hough Transform. Segmentation. Segmentation by Region Growing and Region Splitting. Binary Morphology Operations. Gray Morphology Operations. Image Analysis. Object Recognition by Features. Depth Measurement with Vision Systems. Specialized Lighting. Image Data Compression. Real-Time Image Processing. Heuristics. Applications of Vision Systems. Design project. 9. Fuzzy Logic Control. Fuzzy Control: What is Needed. Crisp Values vs. Fuzzy Values. Fuzzy Sets: Degrees of Membership and Truth. Fuzzification. Fuzzy Inference Rule Base. Defuzzification. Simulation of Fuzzy Logic Controller. Applications of Fuzzy Logic in Robotics. Design Project. Appendix. Matrix Algebra and Notation: A Review. Calculation of an Angle From its Sine, Cosine, or Tangent. Problems. Index.

Modeling, identification, and control of a class of nonlinear systems

Abstract: In this paper, we propose a new fuzzy hyperbolic model for a class of complex systems, which is difficult to model. The fuzzy hyperbolic model is a nonlinear model in nature and can be easily derived from a set of fuzzy rules. It can also be seen as a feedforward neural network model and so we can identify the model parameters by BP-algorithm. We prove that the stable controller can be designed based on linear system theory. Two methods of designing the controller for the fuzzy hyperbolic model are proposed. The results of simulation support the effectiveness of the model and the control scheme.

H/spl infin/ decentralized fuzzy model reference tracking control design for nonlinear interconnected systems

Abstract: In general, due to the interactions among subsystems, it is difficult to design an H/sub /spl infin// decentralized controller for nonlinear interconnected systems. The model reference tracking control problem of nonlinear interconnected systems is studied via H/sub /spl infin// decentralized fuzzy control method. First, the nonlinear interconnected system is represented by an equivalent Takagi-Sugeno type fuzzy model. A state feedback decentralized fuzzy control scheme is developed to override the external disturbances such that the H/spl infin/ model reference tracking performance is achieved. Furthermore, the stability of the nonlinear interconnected systems is also guaranteed. If states are not all available, a decentralized fuzzy observer is proposed to estimate the states of each subsystem for decentralized control. Consequently, a fuzzy observer-based state feedback decentralized fuzzy controller is proposed to solve the H/sub /spl infin// tracking control design problem for nonlinear interconnected systems. The problem of H/sub /spl infin// decentralized fuzzy tracking control design for nonlinear interconnected systems is characterized in terms of solving an eigenvalue problem (EVP). The EVP can be solved very efficiently using convex optimization techniques. Finally, simulation examples are given to illustrate the tracking performance of the proposed methods.

Ten years of genetic fuzzy systems: current framework and new trends

Abstract: Although fuzzy systems demonstrated their ability to solve different kinds of problems in various applications, there is an increasing interest on augmenting them with learning capabilities. Two of the most successful approaches to hybridise fuzzy systems with adaptation methods have been made in the realm of soft computing: neuro-fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The article focuses on genetic fuzzy systems, paying special attention to genetic fuzzy rule based systems, giving a brief overview of the field.

Fuzzy sliding-mode controllers with applications

Abstract: This paper concerns the design of robust control systems using sliding-mode control that incorporates a fuzzy tuning technique. The control law superposes equivalent control, switching control, and fuzzy control. An equivalent control law is first designed using pole placement. Switching control is then added to guarantee that the state reaches the sliding mode in the presence of parameter and disturbance uncertainties. Fuzzy tuning schemes are employed to improve control performance and to reduce chattering in the sliding mode. The practical application of fuzzy logic is proposed here as a computational-intelligence approach to engineering problems associated with sliding-mode controllers. The proposed method can have a number of industrial applications including the joint control of a hydraulically actuated mini-excavator as presented in this paper. The control hardware is described together with simulated and experimental results. High performance and attenuated chatter are achieved. The results obtained verify the validity of the proposed control approach to dynamic systems characterized by severe uncertainties.

A systematic study of fuzzy PID controllers-function-based evaluation approach

Abstract: A function-based evaluation approach is proposed for a systematic study of fuzzy proportional-integral-derivative (PID)-like controllers. This approach is applied for deriving process-independent design guidelines from addressing two issues: simplicity and nonlinearity. To examine the simplicity of fuzzy PID controllers, we conclude that direct-action controllers exhibit simpler design properties than gain-scheduling controllers. Then, we evaluate the inference structures of direct-action controllers in five criteria: control-action composition, input coupling, gain dependency, gain-role change, and rule/parameter growth. Three types of fuzzy PID controllers, using one-, two- and three-input inference structures, are analyzed. The results, according to the criteria, demonstrate some shortcomings in Mamdani's two-input controllers. For keeping the simplicity feature like a linear PID controller, a one-input fuzzy PID controller with "one-to-three" mapping inference engine is recommended. We discuss three evaluation approaches in a nonlinear approximation study: function-estimation-based, generalization-capability-based and nonlinearity-variation-based approximations. The study focuses on the last approach. A nonlinearity evaluation is then performed for several one-input fuzzy PID controllers based on two measures: nonlinearity variation index and linearity approximation index. Using these quantitative indices, one can make a reasonable selection of fuzzy reasoning mechanisms and membership functions without requiring any process information. From the study we observed that the Zadeh-Mamdani's "max-min-gravity" scheme produces the highest score in terms of nonlinearity variations, which is superior to other schemes, such as Mizumoto's "product-sum-gravity" and "Takagi-Sugeno-Kang" schemes.

Switching control of an R/C hovercraft: stabilization and smooth switching

Abstract: This paper presents stable switching control of an radio-controlled (R/C) hovercraft that is a nonholonomic (nonlinear) system. To exactly represent its nonlinear dynamics, more importantly, to maintain controllability of the system, we newly propose a switching fuzzy model that has locally Takagi-Sugeno (T-S) fuzzy models and switches them according to states, external variables, and/or time. A switching fuzzy controller is constructed by mirroring the rule structure of the switching fuzzy model of an R/C hovercraft. We derive linear matrix inequality (LMI) conditions for ensuring the stability of the closed-loop system consisting of a switching fuzzy model and controller. Furthermore, to guarantee smooth switching of control input at switching boundaries, we also derive a smooth switching condition represented in terms of LMIs. A stable switching fuzzy controller satisfying the smooth switching condition is designed by simultaneously solving both of the LMIs. The simulation and experimental results for the trajectory control of an R/C hovercraft show the validity of the switching fuzzy model and controller design, particularly, the smooth switching condition.

Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators

Abstract: Recently, through the use of parameterized fuzzy approximators, various adaptive fuzzy control schemes have been developed to deal with nonlinear systems whose dynamics are poorly understood. An important class of parameterized fuzzy approximators is constructed using radial basis function (RBF) as a membership function. However, some tuneable parameters in RBF appear nonlinearly and the determination of the adaptive law for such parameters is a nontrivial task. In this paper, we propose a new adaptive control method in an effort to tune all the RBF parameters thereby reducing the approximation error and improving control performance. Global boundedness of the overall adaptive system and tracking to within a desired precision are established with the new adaptive controller. Simulations performed on a simple nonlinear system illustrate the approach.

Dynamical cognitive network - an extension of fuzzy cognitive map

Abstract: We present the dynamic cognitive network (DCN) which is an extension of the fuzzy cognitive map (FCM). Each concept in the DCNs can have its own value set, depending on how precisely it needs to be described in the network. This enables the DCN to describe the strength of causes and the degree of effects that are crucial to conducting meaningful inferences. The arcs in the DCN define dynamic, causal relationships between concepts. Structurally, DNCs are scalable and more flexible as compared to FCMs. A DCN can be as simple as a cognitive map and FCM, or as complex as a nonlinear dynamic system. To demonstrate the potential applications of DCNs, we present some simulation results. This paper represents our first attempt to develop a dynamic fuzzy inference system using causal relationships. There are many interesting and challenging theoretical and practical issues in DCNs open to further research.

Adaptive fuzzy-based tracking control for nonlinear SISO systems via VSS and H/sup /spl infin// approaches

Abstract: An adaptive fuzzy-based tracking control equipped with VSS and H/sup /spl infin// control algorithms is proposed for nonlinear SISO systems involving plant uncertainties and external disturbances. Both well-defined VSS indirect and direct adaptive fuzzy-based H/sup /spl infin// control schemes are developed. In order to compensate the effect of the approximation error via the adaptive fuzzy system on the H/sup /spl infin// tracking control, a modified algebraic Riccati-like equation must be solved and consequently it can be shown that all the states and signals of the closed-loop system are bounded and the effect of the external disturbance on the tracking error can be attenuated to any prescribed level. Compared with the previous literature that also addresses the adaptive fuzzy-based tracking control as in this paper, this paper can be extended to handle a larger class of uncertain nonlinear systems by the incorporation of robust VSS and H/sup /spl infin// control techniques. Finally, simulation examples are included to confirm the validity and performance of the proposed control algorithms.

On relaxed LMI-based designs for fuzzy regulators and fuzzy observers

Abstract: Relaxed conditions for the stability study of nonlinear, continuous systems given by fuzzy models are presented. A theoretical analysis shows that the proposed method provides better or at least the same results of the methods presented in the literature. Digital simulations exemplify this fact. These results are also used for the fuzzy regulators and observers design. The nonlinear systems are represented by the fuzzy models proposed by Takagi and Sugeno. The stability analysis and the design of controllers are described by LMIs (Linear Matrix Inequalities), that can be solved efficiently by convex programming techniques. The specification of the decay rate, constraints on control input and output are also described by LMIs. Finally, the proposed design method is applied in the control of an inverted pendulum.