2,183 research outputs found
Aspects of fuzzy control and estimation
No full textThis paper gives an overview of Fuzzy Systems for application to fuzzy modelling, analysis, control and estimation. This first section describes the major concepts together with future research objectives without requiring any a priori knowledge on the part of the reader. Further sections introduce the mathematical notation required to describe fuzzy systems and review existing application and research areas. The subject of performance analysis tools for fuzzy systems is highlighted as a current research area and an example of a simple analysis tool is given. The topic of signal estimation using fuzzy models is also discussed and a target tracking example is included. Comparisons are also drawn between fuzzy systems and single layer associative memory neural networks that offer some transparency for dynamical processes modelled as neural networks
Phase plane analysis tools for a class of fuzzy control systems
No full textAlthough fuzzy controllers have been fully applied with success, one of the reasons they are not used more generally could be the lack of analysis tools. This paper describes a performance prediction and design tool, applicable to a class of systems that have quasi second order behaviour, which is analogous to the algebraic phase plane approach. Using this technique the response of a rule based system can be investigated and the influence of individual ruls on overall performance can be determined, allowing a stability analysis to be carried out directly on the rule based system. Implications for controller design are considered. The tools described are supported by a software package written at Southampton University and used by two UK Ministry of Defence establishments for autonomous vehicle control research
Indirect adaptive fuzzy control
No full textFuzzy controllers may be either static systems, which have fixed rule base, or adaptive systems, which have the ability to alter their rules. A discussion of adaptive fuzzy controllers and a comparison with corresponding algebraic techniques concludes that all previous adaptive fuzzy controllers have been of the direct adaptive type. Such controllers use observations of closed loop control performance to manipulate the controller rule base directly without any intermediate process model being produced. In this paper, an indirect adaptive fuzzy controller is proposed where an intermediate process model, identified for observed data, is used to peform on-line controller design. The resulting separation of the adaptation system from controller design enables learning convergence to be investigated. Examples are given of both fuzzy model identification and controller design for linear and nonlinear processes
Indirect adaptive fuzzy controllers
No full textMany classical control methods are based upon assumptions of linearity and stationarity of the process to be controlled. For the case of motion control of a land vehicle in an unstructured outdoor environment these assumptions do not hold, due to complex vehicle interactions with its surroundings and time-varying environmental conditions. The large number of possible future platforms leads to the desire to produce motion controllers which are generally applicable to a wide range of vehicles with little a priori knowledge of vehicle dynamics. Intelligent, self-learning, systems promise many of the desired features for such controllers. This thesis investigates the use of intelligent controllers for autonomous land vehicle motion control. A new class of fuzzy controller, the indirect adaptive fuzzy controller is proposed as a possible solution to this problem. This controller is then developed by combining on-line adaptive modelling with model causality inversion and on-line controller design. The resulting controller is an analogue of the indirect adaptive algebraic controller. A major advantages of this method is the separation of model convergence and control loops enabling the two aspects to be analysed separately. Demonstration of this work has been achieved by a series of simulation tests using a variety of vehicle models. A conventional front wheel steer road vehicle model has been used as well as two IFAC benchmark control problems (ship autopilot and passenger bus) to investigate the properties of the controller. To test the controller with realistic demand signals, a static rule-based piloting system has also been developed. These simulations have demonstrated i) the successful control of systems with little a priori vehicle knowledge ii) ability to adapt to continuous and sudden parametric changes in the process iii) good noise rejection properties iv) good disturbance rejection properties and v) ability to adapt to stationary loop non-linearities
Apophatic Elements in the Theory and Practice of Psychoanalysis: Pseudo-Dionysius and C.G. Jung
No full textThis thesis identifies apophatic elements in the theory and practice of psychoanalysis through an examination of Pseudo-Dionysius and C.G. Jung. Pseudo-Dionysius brought together Greek and Biblical currents of negative theology and the via negativa. The apophatic concepts and metaphors which appear in the work of Pseudo-Dionysius are identified. The psychology of Jung can be read as a continuation and extension of the apophatic tradition. The presence of neoplatonic themes in Jung’s work is discussed, as well as his references to Pseudo-Dionysius. There is a thorough examination of Jung’s discussion of opposites, including his reception of Nicholas of Cusa’s concept of the coincidence of opposites. The role of the transcendent function in Jung’s psychology is reviewed. The work of contemporary scholars of religion, philosophers and Jungian theorists are compared to Jung’s using the lens of apophasis. There is an exploration of ways in which motifs in Pseudo-Dionysius’ Ecclesiatical Hierarchy resonate with contemporary psychoanalytic psychotherapy. This study demonstrates that apophatic motifs saturate Jung’s work. It provides a platform for research into apophasis in the wider field of psychoanalysis
Intelligent Control: Aspects of Fuzzy Logic and Neural Networks
No full textIndex: 1. An Introduction to Intelligent Control 1.1 Preliminaries 1.2 Intelligent Control Requirements and Architectures 1.3 Approaches to Intelligent Control 1.4 Knowledge Based Systems 1.5 Fuzzy Logic 1.6 Fuzzy Logic in Control 1.7 Neurocontrollers 1.8 Higher Level Intelligent Controllers 1.9 Bibliographical Notes 2. Introductory Fuzzy Logic 2.1 Fuzzy Sets and Logic 2.2 Fuzzy Inference and Composition 2.3 Defuzzification 3. Fuzzy Logic Controller Structure and Design 3.1 Introduction 3.2 Applications of Fuzzy Set Theory 3.3 Fuzzy Logic Controller Structural Issues 3.4 Design Requirements of Fuzzy Logic Controllers 4. The Static Fuzzy Logic Controller 4.1 Introduction 4.2 Controller Design by Verbalisation or Expert Interrogation 4.3 The Fuzzy PID Controller 4.4 Parametrically Determined Fuzzy PID Controllers 4.5 Linguistic Rule Inversion Fuzzy Logic Controllers 4.6 Cluster Based Fuzzy Logic Controllers 5. Self-Organising Fuzzy Logic Control 5.1 Introduction 5.2 Control Rule Base SOFLICs 5.3 Rule Based SOFLIC Applications 5.4 Systematic Design of Control Rule Based SOFLIC 6. Indirect Self-Organising Fuzzy Logic Controllers 6.1 Introduction 6.2 Self-Organising Fuzzy Models and Predictors 6.3 Relation Causality Inversion 6.4 Controller Design 6.5 Adaptive Fuzzy Controller 6.6 A Simulation Example of Indirect Adaptive Fuzzy Logic Control 6.7 Nested and Hybrid Fuzzy Controllers 7. Case Studies of Indirect Adaptive Fuzzy Control 7.1 Regulation of a Ship's Heading 7.2 Track Control of a City Bus 7.3 Autonomous Road Vehicle Control and Guidance 7.4 Observations on Indirect Fuzzy Adaptive Control 8. Neural Network Approximation Capability for Control and Modelling 8.1 Introduction 8.2 Approximation Capability of Artificial Neural Networks 8.3 Multilayer Perceptrons in Neurocontrol 8.4 Radial Basis Functions in Modelling and Control 9. The B-spline Neural Network and Fuzzy Logic 9.1 Introduction 9.2 Polynomial Basis Functions 9.3 B-splines for Guidance 9.4 Multivariate Basis Functions 9.5 Weighted Adaptation 9.6 B-spline Neural Net Nonlinear Time Series Predictors and Modelling 9.7 A Comparison between Fuzzy Logic and Single Layer Associative Memory Neural Networks 9.8 Conclusions Appendix: Mathematical Prerequisites A.1 Metric Spaces A.2 Normed Metric Spaces A.3 Algebras A.4 Approximation in Normed Spaces Content
Marriage record of Ford, James Monroe and Moore, Lula Viola
No full textMarriage license for James Monroe Ford and Lula Viola Moore. C.G. Jones was the Justice of the Peace
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