Fuzzy control systems for thermal processes: synthesis, design and implementation

A completed case study on fuzzy logic control of thermal processes has been carried out using a professional laboratory oven for industrial purpose as an experimental test rig. It involved system engineering design analysis, control synthesis, and implementation as well as application software and signal interface design and development. The resulting expertise and lessons learned are reported in this contribution. The structure of PD type of fuzzy logic controllers is closely discussed along with synthesis issues of membership functions and knowledge rule base. Special software was developed using Microsoft Visual Studio, C++ and Visual basic for GUI for a standard PC platform. The application software designed and implemented has four modules: FIS editor, Rule Editor, Membership Function Editor and Fuzzy Controller with Rule Viewer. Quality and performance of the overall fuzzy process control system have been investigated and validated to fulfill the required quality specification

Design and analysis of real intelligent mapping systems with applications to systems and control.

by Yeung Wai Leung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 92-[96]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Fuzzy Inference and Real Intelligent Mapping --- p.1Chapter 1.2 --- Organization of the thesis --- p.5Chapter 2 --- Fuzzy Logic inference --- p.7Chapter 2.1 --- Fuzzy logic --- p.7Chapter 2.1.1 --- Fuzzy sets --- p.7Chapter 2.1.2 --- Operations on fuzzy sets --- p.10Chapter 2.2 --- Fuzzy Inference --- p.11Chapter 3 --- Weaknesses of fuzzy inference --- p.17Chapter 3.1 --- Is the use of linguistic fuzzy if-then rules and membership func- tions a good means of representing human expert knowledge? --- p.17Chapter 3.2 --- Role of conventional fuzzy inference doubtful if the expert knowl- edge is in the form of sampled input-output data --- p.21Chapter 3.3 --- Computational requirements --- p.23Chapter 3.4 --- Low transparency --- p.24Chapter 3.5 --- Analytical difficulties --- p.25Chapter 4 --- Real Intelligent Mapping --- p.27Chapter 5 --- Design of Real Intelligent Mapping Systems Using Dirichlet Tessellation --- p.33Chapter 5.1 --- Dirichlet tessellation for function approximation --- p.34Chapter 5.2 --- Identification of a DT based RIM system by least-squares --- p.42Chapter 5.3 --- Examples --- p.48Chapter 5.3.1 --- Defining the problem --- p.48Chapter 5.3.2 --- Balancing an inverted pendulum --- p.49Chapter 5.3.3 --- Balancing an inverted pendulum with cart --- p.53Chapter 5.3.4 --- Truck backing-up --- p.56Chapter 5.3.5 --- Chaotic time series prediction --- p.60Chapter 5.4 --- Interactive CAD platform for RIM systems design --- p.63Chapter 6 --- Analysis of Dirichlet tessellation based Real Intelligent Mapping Systems --- p.67Chapter 6.1 --- Local Stability Analysis of DT Based RIM Systems --- p.69Chapter 6.1.1 --- Balancing an inverted pendulum --- p.71Chapter 6.1.2 --- Truck backing-up --- p.73Chapter 6.2 --- Global stability analysis of DT based RIM systems --- p.74Chapter 6.3 --- Design of a stable DT based RIM system --- p.79Chapter 6.4 --- A method for analyzing Second order DT based RIM systems --- p.82Chapter 6.5 --- Piecewise-polynomial real domain representation of a class of fuzzy controller and its stability --- p.85Chapter 7 --- Conclusion --- p.90Bibliography --- p.9

An expert fuzzy logic controller employing adaptive learning for servo systems

An expert fuzzy logic controller with adaptive learning is proposed as an intelligent controller for servo systems. A key component of this controller is an adaptive learning mechanism which is used to self-regulate the scaling factors and the control action based on the error between the desired value and the plant output. The inference engine of this controller is based on the principle of approximate reasoning and the learning strategy is based on reinforcement learning. A novel approach of model reference adaptive control is also proposed for servo systems. The comparison of the performance between the proposed controller and PID controllers is discussed. The simulation results show that the performance of the proposed controller is better than the conventional approach or previous research. The real-time application demonstrates that a faster response of a servo system can be achieved. Furthermore, the proposed controller is relatively insensitive to variations in the parameters of control systems

Intelligent control of a class of nonlinear systems

The objective of this study is to improve and propose new fuzzy control algorithms for a class of nonlinear systems. In order to achieve the objectives, novel stability theorems as well as modeling techniques are also investigated. Fuzzy controllers in this work are designed based on the fuzzy basis function neural networks and the type-2 Takagi-Sugeno fuzzy models. For a class of single-input single-output nonlinear systems, a new stability condition is derived to facilitate the design process of proportional-integral Mamdani fuzzy controllers. The stability conditions require a new technique to calculate the dynamic gains of nonlinear systems represented by fuzzy basis function network models. The dynamic gain of a fuzzy basis function network can be approximated by finding the maximum of norm values of the locally linearized systems or by solving a non-smooth optimal control problem. Based on the new stability theorem, a multilevel fuzzy controller with self-tuning algorithm is proposed and simulated in a tower crane control system. For a class of multi-input multi-output nonlinear systems with measurable state variables, a new method for modeling unstructured uncertainties and robust control of unknown nonlinear dynamic systems is proposed by using a novel robust Takagi-Sugeno fuzzy controller. First, a new training algorithm for an interval type-2 fuzzy basis function network is presented. Next, a novel technique is derived to convert the interval type-2 fuzzy basis function network to an interval type-2 Takagi-Sugeno fuzzy model. Based on the interval type-2 Takagi-Sugeno and type-2 fuzzy basis function network models, a robust controller is presented with an adjustable convergence rate. Simulation results on an electrohydraulic actuator show that the robust Takagi-Sugeno fuzzy controller can reduce steady-state error under different conditions while maintaining better responses than the other robust sliding mode controllers can. Next, the study presents an implementation of type-2 fuzzy basis function networks and robust Takagi-Sugeno fuzzy controllers to data-driven modeling and robust control of a laser keyhole welding process. In this work, the variation of the keyhole diameter during the welding process is approximated by a type-2 fuzzy-basis-function network, while the keyhole penetration depth is modelled by a type-1 fuzzy basis function network. During the laser welding process, a CMOS camera integrated with the welding system was used to provide a feedback signal of the keyhole diameter. An observer was implemented to estimate the penetration depth in real time based on the adaptive divided difference filter and the feedback signal from the camera. A robust Takagi-Sugeno fuzzy controller was designed based on the fuzzy basis function networks representing the welding process with uncertainties to adjust the laser power to ensure that the penetration depth of the keyhole is maintained at a desired value. Experimental results demonstrated that the fuzzy models provided an accurate estimation of both the welding geometry and its variations due to uncertainties, and the robust Takagi-Sugeno fuzzy controller successfully reduced the penetration depth variation and improved the quality of the welding process