Analysis and Applications of the Km Algorithm in Type-2 Fuzzy Logic Control and Decision Making

Ph.DDOCTOR OF PHILOSOPH

Neuro-fuzzy software for intelligent control and education

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Automação). Faculdade de Engenharia. Universidade do Porto. 200

NETWORKED FUZZY CONTROL SYSTEM FOR A HIGH-PERFORMANCE DRILLING PROCESS

ABSTRACT In order to improve drilling efficiency while preserving tool life, the current study focuses on the design and implementation of a simple, optimal fuzzy-control system for drilling force. The main topic of this study is the design and implementation of a networked fuzzy controller. The control system consists of a two-input (force error and change of error), single-output (feedrate increment) fuzzy controller. The control algorithm is connected to the process through a multipoint interface (MPI) bus. The output (i.e., feed-rate) signal is transmitted through the MPI; therefore, networkinduced delay is unavoidable. The optimal tuning of the fuzzy controller using a maximum known delay is based on the integral time absolute error (ITAE) criterion. The main advantage of the approach presented herein is the design of a simple fuzzy controller using a known maximum allowable delay to deal with uncertainties and nonlinearities in the drilling process and delays in the network-based application. The results demonstrate that the proposed control strategy provides an excellent transient response without overshoot and a slightly higher drilling time than the CNC working alone (uncontrolled). Therefore, the fuzzy-control system reduces the influence of the increase in cutting force and torque that occurs as the drill depth increases, thus eliminating the risk of rapid drill wear and catastrophic drill breakage

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies

Switching control systems and their design automation via genetic algorithms

The objective of this work is to provide a simple and effective nonlinear controller. Our strategy involves switching the underlying strategies in order to maintain a robust control. If a disturbance moves the system outside the region of stability or the domain of attraction, it will be guided back onto the desired course by the application of a different control strategy. In the context of switching control, the common types of controller present in the literature are based either on fuzzy logic or sliding mode. Both of them are easy to implement and provide efficient control for non-linear systems, their actions being based on the observed input/output behaviour of the system. In the field of fuzzy logic control (FLC) using error feedback variables there are two main problems. The first is the poor transient response (jerking) encountered by the conventional 2-dimensional rule-base fuzzy PI controller. Secondly, conventional 3-D rule-base fuzzy PID control design is both computationally intensive and suffers from prolonged design times caused by a large dimensional rule-base. The size of the rule base will increase exponentially with the increase of the number of fuzzy sets used for each input decision variable. Hence, a reduced rule-base is needed for the 3-term fuzzy controller. In this thesis a direct implementation method is developed that allows the size of the rule-base to be reduced exponentially without losing the features of the PID structure. This direct implementation method, when applied to the reduced rule-base fuzzy PI controller, gives a good transient response with no jerking

Fuzzy control in manufacturing systems

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Modelling and design of the eco-system of causality for real-time systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The purpose of this research work is to propose an improved method for real-time sensitivity analysis (SA) applicable to large-scale complex systems. Borrowed from the EventTracker principle of the interrelation of causal events, it deploys the Rank Order Clustering (ROC) method to automatically group every relevant system input to parameters that represent the system state (i.e. output). The fundamental principle of event modelling is that the state of a given system is a function of every acquirable piece of knowledge or data (input) of events that occur within the system and its wider operational environment unless proven otherwise. It therefore strives to build the theoretical and practical foundation for the engineering of input data. The event modelling platform proposed attempts to filter unwanted data, and more importantly, include information that was thought to be irrelevant at the outset of the design process. The underpinning logic of the proposed Event Clustering technique (EventiC) is to build causal relationship between the events that trigger the inputs and outputs of the system. EventiC groups inputs with relevant corresponding outputs and measures the impact of each input variable on the output variables in short spans of time (relative real-time). It is believed that this grouping of relevant input-output event data by order of its importance in real-time is the key contribution to knowledge in this subject area. Our motivation is that components of current complex and organised systems are capable of generating and sharing information within their network of interrelated devices and systems. In addition to being an intelligent recorder of events, EventiC could also be a platform for preliminary data and knowledge construction. This improvement in the quality, and at times the quantity of input data, may lead to improved higher level mathematical formalism. It is hoped that better models will translate into superior controls and decision making. It is therefore believed that the projected outcome of this research work can be used to predict, stabilize (control), and optimize (operational research) the work of complex systems in the shortest possible time. For proof of concept, EventiC was designed using the MATLAB package and implemented using real-time data from the monitoring and control system of a typical cement manufacturing plant. The purpose for this deployment was to test and validate the concept, and to demonstrate whether the clusters of input data and their levels of importance against system performance indicators could be approved by industry experts. EventiC was used as an input variable selection tool for improving the existing fuzzy controller of the plant. Finally, EventiC was compared with its predecessor EventTracker using the same case study. The results revealed improvements in both computational efficiency and the quality of input variable selection

Fuzzy Inference Systems for Risk Appraisal in Military Operational Planning

Advances in computing and mathematical techniques have given rise to increasingly complex models employed in the management of risk across numerous disciplines. While current military doctrine embraces sound practices for identifying, communicating, and mitigating risk, the complex nature of modern operational environments prevents the enumeration of risk factors and consequences necessary to leverage anything beyond rudimentary risk models. Efforts to model military operational risk in quantitative terms are stymied by the interaction of incomplete, inadequate, and unreliable knowledge. Specifically, it is evident that joint and inter-Service literature on risk are inconsistent, ill-defined, and prescribe imprecise approaches to codifying risk. Notably, the near-ubiquitous use of risk matrices (along with other qualitative methods), are demonstrably problematic at best, and downright harmful at worst, due to misunderstanding and misapplication of their quantitative implications. The use of fuzzy set theory is proposed to overcome the pervasive ambiguity of risk modeling encountered by today’s operational planners. Fuzzy logic is adept at addressing the problems caused by imperfect and imprecise knowledge, entangled causal relationships, and the linguistic input of expert opinion. To this end, a fuzzy inference system is constructed for the purpose of risk appraisal in military operational planning

Design of stable adaptive fuzzy control.

by John Tak Kuen Koo.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 217-[220]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- "Robust, Adaptive and Fuzzy Control" --- p.2Chapter 1.3 --- Adaptive Fuzzy Control --- p.4Chapter 1.4 --- Object of Study --- p.10Chapter 1.5 --- Scope of the Thesis --- p.13Chapter 2 --- Background on Adaptive Control and Fuzzy Logic Control --- p.17Chapter 2.1 --- Adaptive control --- p.17Chapter 2.1.1 --- Model reference adaptive systems --- p.20Chapter 2.1.2 --- MIT Rule --- p.23Chapter 2.1.3 --- Model Reference Adaptive Control (MRAC) --- p.24Chapter 2.2 --- Fuzzy Logic Control --- p.33Chapter 2.2.1 --- Fuzzy sets and logic --- p.33Chapter 2.2.2 --- Fuzzy Relation --- p.40Chapter 2.2.3 --- Inference Mechanisms --- p.43Chapter 2.2.4 --- Defuzzification --- p.49Chapter 3 --- Explicit Form of a Class of Fuzzy Logic Controllers --- p.51Chapter 3.1 --- Introduction --- p.51Chapter 3.2 --- Construction of a class of fuzzy controller --- p.53Chapter 3.3 --- Explicit form of the fuzzy controller --- p.57Chapter 3.4 --- Design criteria on the fuzzy controller --- p.65Chapter 3.5 --- B-Spline fuzzy controller --- p.68Chapter 4 --- Model Reference Adaptive Fuzzy Control (MRAFC) --- p.73Chapter 4.1 --- Introduction --- p.73Chapter 4.2 --- "Fuzzy Controller, Plant and Reference Model" --- p.75Chapter 4.3 --- Derivation of the MRAFC adaptive laws --- p.79Chapter 4.4 --- "Extension to the Multi-Input, Multi-Output Case" --- p.84Chapter 4.5 --- Simulation --- p.90Chapter 5 --- MRAFC on a Class of Nonlinear Systems: Type I --- p.97Chapter 5.1 --- Introduction --- p.98Chapter 5.2 --- Choice of Controller --- p.99Chapter 5.3 --- Derivation of the MRAFC adaptive laws --- p.102Chapter 5.4 --- Example: Stabilization of a pendulum --- p.109Chapter 6 --- MRAFC on a Class of Nonlinear Systems: Type II --- p.112Chapter 6.1 --- Introduction --- p.113Chapter 6.2 --- Fuzzy System as Function Approximator --- p.114Chapter 6.3 --- Construction of MRAFC for the nonlinear systems --- p.118Chapter 6.4 --- Input-Output Linearization --- p.130Chapter 6.5 --- MRAFC with Input-Output Linearization --- p.132Chapter 6.6 --- Example --- p.136Chapter 7 --- Analysis of MRAFC System --- p.140Chapter 7.1 --- Averaging technique --- p.140Chapter 7.2 --- Parameter convergence --- p.143Chapter 7.3 --- Robustness --- p.152Chapter 7.4 --- Simulation --- p.157Chapter 8 --- Application of MRAFC scheme on Manipulator Control --- p.166Chapter 8.1 --- Introduction --- p.166Chapter 8.2 --- Robot Manipulator Control --- p.170Chapter 8.3 --- MRAFC on Robot Manipulator Control --- p.173Chapter 8.3.1 --- Part A: Nonlinear-function feedback fuzzy controller --- p.174Chapter 8.3.2 --- Part B: State-feedback fuzzy controller --- p.182Chapter 8.4 --- Simulation --- p.186Chapter 9 --- Conclusion --- p.199Chapter A --- Implementation of MRAFC Scheme with Practical Issues --- p.203Chapter A.1 --- Rule Generation by MRAFC scheme --- p.203Chapter A.2 --- Implementation Considerations --- p.211Chapter A.3 --- MRAFC System Design Procedure --- p.215Bibliography --- p.21

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control, and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making