CDC2000 New Approaches to Fuzzy Control

New Approaches to Fuzzy Control
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation Adaptive Control: Linear Systems Estimation and Closed Loop Identification Control of Markov Processes Nonlinear Filtering and Control Modelling, Identification and Validation of Nonlinear Systems Differential Geometric Control Theory for Mechanical Systems Nonlinear Output Feedback Control Pneumatics and Compression Systems Control of Quantum Phenomena II Stability of Hybrid Systems Performance Analysis in Communication Networks Adaptive Control of Nonlinear Systems LMI Methods in Design Robust Control of Time Delay Systems Subspace Identification Methods Nonlinear Stochastic Filtering and Estimation Bifurcations, Chaos and Control I New Progress in Synthesis of Nonlinear Systems I Implementation Issues of Sliding Mode Control Theory Control of Mixing in Shear Flows Novel Neural Network Control Techniques for Industrial Motion Control Systems Physiological Control Systems Optimal Control of Hybrid Systems Stochastic Models for Communication Networks Control and Stabilisation of Nonlinear Systems New Directions in Robust Control Linear Systems Theory Advanced Topics in Systems Theory Estimation in Action Bifurcations, Chaos and Control II New Progress in Synthesis of Nonlinear Systems II Numerical Design and Analysis Techniques for Nonlinear Systems Analysis and Control of Underactuated Systems Sliding Mode Control I Challenges in the Application of Control to Computer Systems Estimation and Diagnosis of Discrete Event Systems Communications and Games Optimal Control Stochastic Systems Model Reduction Methodologies Identification and Subspace Methods Applications of Nonlinear Adaptive Control Advances in Nonlinear Output Feedback Design The Behavioural Approach to Systems and Control Vision Based Estimation and Control: Recent Advances and Open Problems Agile Control of Military Operations Sliding Mode Control II Model-based Fault Diagnosis of Industrial Processes Discrete Event Systems / Petri Nets System Identification and Confidence Estimation New Approaches to H-Infinity Control I Probabilistic Approaches to Robust Control Time Delay System Stabilisation Identification Methods Controlled Stochastic Processes Output Feedback of Nonlinear Systems Topics in Nonlinear Stabilisation Mobile Robots: Tracking Control Robust Control of Nonlinear Systems Power Systems Stabilisation and Control Disk Drive Control Hybrid Control Applications Discrete Time Systems New Approaches to H-Infinity Control II Linear Systems with Saturating Actuators New Theories in Distributed Parameter Systems Applications of Estimation and Identification Stochastic Control and Tuning Methodologies Control of Nonlinear Systems Iterative Learning and Control Coordinating Robot Systems Nonlinear Time Varying Systems Novel Applications of Neural Networks Aerospace Applications Switched Systems Implicit and Descriptor Systems LQG Periodic Systems and Disturbances New Horizons for Distributed Parameter Systems State Estimation Learning and Neuro-Control Nonlinear Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	A Control Sequence Generator For Fuzzy Gain Schedulers Authors: Rainer Palm, Christiane Stutz, Thomas Runkler, Volume: 1, Page 370 Paper number 1252 Abstract: The generation of the sequence of control inputs along a given state trajectory for a nonlinear system is described. A nonlinear system is linearized at predefined points in the product space of the states and control inputs and then approximated by local linear fuzzy models. Based on this approximation the system is controlled by a set of local linear Takagi-Sugeno fuzzy controllers. The local control laws designed for the error system incorporate both the desired and the actual state as well as the corresponding control input. Normally, the desired state is defined by the user but the related control input cannot always be calculated in a unique way especially for a non-square system. The proposed method generates the desired control inputs on the basis of the states and its derivatives using inverse fuzzy models of the system. In an optimization loop the control inputs are corrected by the analytical forward model of the nonlinear system. CD001252.PDF (From Author) TOP Self-Tuning Fuzzy Looper Control for Rolling Mills Authors: Farrokh Janabi-Sharifi, Jiang Fan, Volume: 1, Page 376 Paper number 1312 Abstract: This paper deals with the problem of looper control for tension-free rolling. Conventional controllers cannot deal effectively with unmodeled dynamics and large variations which can lead to scrap runs and damages to machinery. Therefore, a fuzzy controller has been designed to use the expert knowledge of the operators for disturbed process control. Also, a self-tuning algorithm is incorporated for both on-line and off-line tuning of the fuzzy membership functions. This paper discusses the design of the fuzzy logic controller and its self-tuning. The effects of various design options are discussed and practical conclusions are made. Results from simulations are also presented. CD001312.PDF (From Author) TOP Analysis of Fuzzy T-S Observers and Controllers from the Interpolation Perspective Authors: Akram M. Fayaz, Volume: 1, Page 382 Paper number 1881 Abstract: In this paper T-S fuzzy observers and controllers, built for the plants represented by T-S models, are analyzed from the interpolation viewpoint. The notion of fuzzy stability covering condition is defined extending the definition given in the "classical interpolation" case. Assuming slow variations of a so-called interpolating variable, sufficient conditions are given in order to generate stable parameter-varying family of observers and controllers that estimate the states and, stabilize nonlinear plants. CD001881.PDF (From Author) TOP A Velocity Control Of Frictional Servosystems Using An Adaptive Fuzzy Control Authors: Chih-Lyang Hwang, Volume: 1, Page 388 Paper number 38 Abstract: Based on the system relative degree, the frictional servosystem is transformed into external and internal parts. By using a feedback linearizing control, the external part becomes a linear dynamic system with uncertainties. A reference model with the desired amplitude and phase properties is given to obtain an error dynamics in the presence of uncertainties. The unmatched uncertainty is also examined. To improve the system performance, an on-line fuzzy-model is employed to model these uncertainties in a compact subset. An updating law with e-modification for the weight of fuzzy-model is designed to obtain an effective learning of the uncertainties. Then, an equivalent control using the known part of frictional servosystem and the learning fuzzy-model of uncertainties is established to achieve the desired result. The unmodeled dynamics caused by the error of approximated fuzzy-model and estimated weight are tackled by a switching control. In summary, the adaptive fuzzy control includes two parts: a feedback linearizing control with a reference model and an adaptive fuzzy variable structure control (AFVSC). The stability of the overall system is then verified by the Lyapunov theory so that the uniformly ultimately bounded tracking is accomplished. Simulations are also presented to verify the usefulness of the proposed control. CD000038.PDF (From Author) CD000038.PDF (Scanned) TOP Universal Fuzzy Controllers For Discrete Time Systems Authors: Gang Feng, Volume: 1, Page 394 Paper number 1007 Abstract: In this paper we address the issues of universal fuzzy controllers for discrete time systems. We first present a universal function approximation theorem based on a fuzzy dynamic model. Then we show the results of universal fuzzy controllers for a large class of nonlinear systems. CD001007.PDF (From Author) TOP Design Of A Fuzzy Controller Using Random Signal-Based Learning Employing Simulated Annealing Authors: Chang-wook Han, Jung-il Park, Volume: 1, Page 396 Paper number 1088 Abstract: This paper describes the application of simulated annealing to a random signal-based learning. Simulated annealing is used to generate the reinforcement signal of the random signal-based learning. The validity of the proposed algorithm is confirmed by applying it to the control of the inverted pendulum using fuzzy controller and finding the minimum of the nonlinear function. CD001088.PDF (From Author) TOP Satisfactory Optimization Control with Fuzzy Constraints and Goals Authors: Shaoyuan Li, Yugeng Xi, Volume: 1, Page 398 Paper number 114 Abstract: Abstract This paper presented a satisfactory optimization algorithm with fuzzy goals and fuzzy constraints under the framework of predictive control, it allows for more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Compared to the standard quadratic objective function, the designer has more freedom in specifying the desired process behavior. The main idea of the satisfactory control is to satisfy the multi-requirement of production with limited manipulated degree of freedom, inducing the performance indexes optimization and various soft and hard constraints. The importance for different requirements is defined by decision-maker and guaranteed by the control algorithm, to construct a man-machine cooperative control mode to make user satisfactory. By defining the membership degree of the control objective and system constraint, and using the fuzzy interference, the optimal control problem with constraint, multi-objective multi-degree of freedom can be transferred as a convex optimal problem, so as to utilize the efficient optimal algorithm and guarantee the global optimal solution. More importantly, we can increase the freedom degree of control by adjusting the relevant membership degree parameters of control objective and system constraints. The designer's experience of control objective and system constraint can be utilized through the fuzzy inference of language variables, thus can be get better understanding of effect for control performance. CD000114.PDF (From Author) TOP