Thesis (M.Eng.)(Control)--University of Pretoria, 2004. / Summaries in English and Afrikaans. Includes bibliographical references.

Thesis (doctoral)--Universität Augsburg, 2000. / Includes bibliographical references (p. 177-179) and index.

Thesis (Ph. D.)--Drexel University, 2005. / Includes abstract and vita. Includes bibliographical references (leaves 120-128).

Thesis (M.S.)--Ohio University, June, 2005. / Title from PDF t.p. Includes bibliographical references (p. 90-92)

Thesis (M.S.)--Ohio University, November, 2006. / Title from PDF t.p. Includes bibliographical references.

Thesis (Ph. D.)--University of Glasgow, 1994. / Includes bibliographical references. Print version also available.

Thesis (Ph. D.)--University of Glasgow, 1997. / Includes bibliographical references. Print version also available.

Thesis submitted in fulfilment of the requirements for the degree Master of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology / In the past decade, the need for flexibility and reconfigurability in automation has contributed to the rise of the distributed concept in control systems engineering. The IEC 61499 standard is used to define a distributed model for dividing various components of an industrial application in automation process and complicated control of machinery into function blocks. Such function blocks have the flexibility to be distributed and interconnected across a number of controllers. However, this new standard for automation faces two main challenges: the complexity in designs of distributed systems and the lack of utilization of the standard in industry. Most applications of controllers based on functional block programming are for linear systems. As most of industrial processes are nonlinear there is a need to extend the functional block approach for implementation of nonlinear controllers. Design complexity involves the exact modeling of the system in function blocks to obtain its accurate behaviour and the lack of utilization of the standard is understandable because new technologies are not easily accepted in industry due to their high prices and risks of compromising the performance at the production level. The thesis describes a methodology for design and implementation of nonlinear controllers for nonlinear plants in IEC 61499 standard compliant real-time environment of TwinCAT 3 and Beckhoff Programmable Logic Controller (PLC). The first step is to design the nonlinear controllers and simulate the closed-loop system in MATLAB/SIMULINK software. Then the new engineering based concepts to transform the obtained closed-loop system model to an IEC 61499 Function Block Model. This is accomplished by applying one method which involves a complete model transformation between two block-diagram languages: Simulink and TwinCAT 3. The development tools that support the transformation algorithm in the thesis sets the foundation stone of the verification and validation structure for IEC 61499 function blocks approach. The transformed model of the closed-loop system is downloaded to the Beckhoff PLC and is simulated in real-time. The obtained results demonstrate that the developed methodology allows complex nonlinear controllers to be successfully transformed to IEC 61499 standard compliant environment and to be applied for real-time PLC control of complex plants.

M.Sc. (Mathematical Statistics) / This dissertation deals with the computation of nonlinear control strategies for an autonomous vehicle. The vehicle consists of two wheels attached to an axle. It is assumed that both wheels roll without slipping leading to nonholonomic constraints. A third order nonlinear kinematic model of the vehicle is derived from these constraints. It is further assumed that the vehicle has builtin feedback controllers independently regulating the rotational velocities of the wheels (using electric motors as actuators). Thus, the vehicle is maneuvered by applying a separate rotational velocity reference command to the feedback controller of each wheel. The closed loop system dynamics from the reference command to the wheel rotational velocity is approximately modelled by a first order system. This leads to a fifth order nonlinear state-space model for the vehicle. The above-mentioned reference commands constitute the control input variables of the vehicle model and are subject to amplitude constraints. Firstly, a methodology is developed for computing reference command strategies to drive the autonomous vehicle from a specified initial state to a desired final state in a given time and such that a circular obstacle is avoided. The vehicle performs the required maneuver whilst satisfying all the specifications and constraints. Secondly, feedback reference command laws are developed such that a specified point just ahead of the vehicle asymptotically tracks a given reference trajectory in the horizontal plane. The feedback control law steers the vehicle onto the reference path from any initial position and keeps it moving on the path. Thirdly, the stochastic system performance is evaluated when the above-mentioned tracking control strategy is applied and the initial state of the vehicle is a random vector.

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