Within the rapidly growing field of hot sheet metal forming and metal bulk forming the demand arises for fully three-dimensionally tailored properties at the microstructural level, nevertheless, reaching a predefined geometry with such tailored properties puts high requirements on the control mechanisms utilized in the process chain for combined heating, metal forming, and cooling processes. Therefore, the underlying control architecture needs to be freely configurable with respect to a predefined database being extendible to new geometries, microstructural distributions, and materials. The combined control of locally and temporally differential thermo-mechanical effects during the process flow needs to be based on an adaptive algorithm adjusting the process flow in real-time according to predefined parameters delivered by the aforementioned material, geometry, and microstructure property database. The interplay between measurement techniques and adaptive control processes for hot metal forming of functionally graded materials is to be investigated in order to achieve the predefined fully three-dimensional microstructure in complex geometries and optimize the process cycle time in a freely configurable control architecture being customizable to new requirements and materials, resulting in a precision manufacturing process. The emphasis within the given thesis will be on adaptive control strategies embedded within a flexible control architecture for an innovative thermo-mechanical production process embracing induction heating to predefined spatial and temporal temperature distributions, transfer, and combined metal forming as well as heat extraction processes. The flexible control architecture assures an invariant quality for the highly dynamic processes and, moreover, yields extendibility to new materials, geometries, and microstructural distributions.

Large scale manufacturing systems are often run with constant process parameters although continuous and abrupt disturbances influence the process. To reduce quality variations and scrap, a closed-loop control of the process variables becomes indispensable. In this thesis, a modeling and control framework for multistage manufacturing systems is developed, in which the systems are subject to abrupt faults, such as component defects, and continuous disturbances. In this context, three main topics are considered: the development of a modeling framework, the design of robust distributed controllers, and the application of both to the models of a real hot stamping line. The focus of all topics is on the control of the product properties considering the available knowledge of faults and disturbances.

The current paper establishes an axisymmetric model for an inductive heating process. Therein, the fully coupled MAXWELL equations, assuming a temperature dependent permeability, are combined with the non-linear heat conduction equation to yield a monolithic solution strategy. The latter is based on a consistent linearization together with a higher order finite element discretization using GALERKIN'S method in space. For the temporal discretization, the generalized Newmark-? methods, higher order RUNGE-KUTTA methods, and discontinuous and continuous GALERKIN methods are used. Furthermore, the residual error is introduced to open an alternative way to obtain a numerically efficient estimation of the time integration accuracy. Simulation results of the electric, magnetic and thermal fields are provided, together with parameter studies concerning spatial discretization, frequency dependence and penetration depth of the heating zone. Another topic analyzed is the residual error and its estimation quality regarding polynomial degree and time step size. A further aspect of this work is the investigation of the thermal fluid-structure interaction with respect to functionally graded materials. Different coupling strategies for the acceleration of the fixed-point iteration in each time step is in the foreground. Relaxation methods as well as extrapolation methods make it possible to significantly reduce the number of fixed point iterations. At the same time, an adaptive strategy with higher order RUNGE-KUTTA methods can provide a further advantage in combination with acceleration methods.

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Designed to meet the needs of a wide audience without sacrificing mathematical depth and rigor, Adaptive Control Tutorial presents the design, analysis, and application of a wide variety of algorithms that can be used to manage dynamical systems with unknown parameters. Its tutorial-style presentation of the fundamental techniques and algorithms in adaptive control make it suitable as a textbook. Adaptive Control Tutorial is designed to serve the needs of three distinct groups of readers: engineers and students interested in learning how to design, simulate, and implement parameter estimators and adaptive control schemes without having to fully understand the analytical and technical proofs; graduate students who, in addition to attaining the aforementioned objectives, also want to understand the analysis of simple schemes and get an idea of the steps involved in more complex proofs; and advanced students and researchers who want to study and understand the details of long and technical proofs with an eye toward pursuing research in adaptive control or related topics. The authors achieve these multiple objectives by enriching the book with examples demonstrating the design procedures and basic analysis steps and by detailing their proofs in both an appendix and electronically available supplementary material; online examples are also available. A solution manual for instructors can be obtained by contacting SIAM or the authors. Preface; Acknowledgements; List of Acronyms; Chapter 1: Introduction; Chapter 2: Parametric Models; Chapter 3: Parameter Identification: Continuous Time; Chapter 4: Parameter Identification: Discrete Time; Chapter 5: Continuous-Time Model Reference Adaptive Control; Chapter 6: Continuous-Time Adaptive Pole Placement Control; Chapter 7: Adaptive Control for Discrete-Time Systems; Chapter 8: Adaptive Control of Nonlinear Systems; Appendix; Bibliography; Index