This thesis describes work completed on the application of H controller synthesis to the design of controllers for single axis high speed independent drive design examples. H controller synthesis was used in a single controller format and in a self-tuning regulator, a type of adaptive controller. Three types of industrial design examples were attempted using H controller synthesis, both in simulation and on a Drives Test Facility at Aston University. The results were benchmarked against a Proportional, Integral and Derivative (PID) with velocity feedforward controller (VFF), the industrial standard for this application. An analysis of the differences between a H and PID with VFF controller was completed. A direct-form H controller was determined for a limited class of weighting function and plants which shows the relationship between the weighting function, nominal plant and the controller parameters. The direct-form controller was utilised in two ways. Firstly it allowed the production of simple guidelines for the industrial design of H controllers. Secondly it was used as the controller modifier in a self-tuning regulator (STR). The STR had a controller modification time (including nominal model parameter estimation) of 8ms. A Set-Point Gain Scheduling (SPGS) controller was developed and applied to an industrial design example. The applicability of each control strategy, PID with VFF, H, SPGS and STR, was investigated and a set of general guidelines for their use was determined. All controllers developed were implemented using standard industrial equipment.

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Bibliography: leaves 329-348 / xxi, 348 leaves : ill ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1987

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Prompted by the desire to increase the industrial applicability range of self-tuning control, the objective of this work has been to extend the standard linear self-tuning framework to facilitate the design of self-tuning controllers for bilinear systems. Bilinear systems form a well structured class of non-linear systems within which linear systems coexist as a special subclass. They are, therefore, appropriate for modelling a wider range of processes and plant than the restrictive, yet convenient, linear model structures since such models are valid both within the linear subregion and beyond. In addition to extending the self-tuning framework for bilinear systems another significant contribution of the Thesis is the introduction of a cautious least squares estimation procedure which also enhances the existing linear self-tuning schemes.

Over the last decade, considerable effort has been devoted to the implementation and analysis of self-tuning controllers on systems which are assumed to be represented exactly by linear dynamical models. In this thesis we examine the robustness of the self-tuning controller, when applied to systems consisting of a nominal linear plant which may have linear or nonlinear perturbations. Robust stability is the primary criterion and most of the results are for the Clarke-Gawthrop version of the self-tuning controller. Conditions are derived for the robust stability of the adaptively controlled system in terms of the design choices available to the engineer setting up the self-tuning controller. These are strong stability results in that they are in terms of both 1₂ and 1_∞ stability. The results are shown to be applicable to the general delay case and in the presence of non-zero mean disturbances. Preliminary results are also obtained for the robust stability of the explicit self-tuning controller.

Thesis (Ph.D.)--University of Wyoming, 2007. / Title from PDF title page (viewed on June 22, 2009). Includes bibliographical references (p. 120-127).

In this thesis, a Reinforcement Learning (RL) method called Sarsa is used to dynamically tune a PI-controller for a Continuous Stirred Tank Heater (CSTH) experimental setup. The proposed approach uses an approximate model to train the RL agent in the simulation environment before implementation on the real plant. This is done in order to help the RL agent initially start from a reasonably stable policy. Learning without any information about the dynamics of the process is not practically feasible due to the great amount of data (time) that the RL algorithm requires and safety issues. The process in this thesis is modeled with a First Order Plus Time Delay (FOPTD) transfer function, because almost all of the chemical processes can be sufficiently represented by this class of transfer functions. The presence of a delay term in this type of transfer functions makes them inherently more complicated models for RL methods. RL methods should be combined with generalization techniques to handle the continuous state space. Here, parameterized quadratic function approximation compounded with k-nearest neighborhood function approximation is used for the regions close and far from the origin, respectively. Applying each of these generalization methods separately has some disadvantages, hence their combination is used to overcome these flaws. The proposed RL-based PI-controller is initially trained in the simulation environment. Thereafter, the policy of the simulation-based RL agent is used as the starting policy of the RL agent during implementation on the experimental setup. As a result of the existing plant-model mismatch, the performance of the RL-based PI-controller using this primary policy is not as good as the simulationresults; however, training on the real plant results in a significant improvement in this performance. On the other hand, the IMC-tuned PI-controllers, which are the most commonly used feedback controllers are also compared and they also degrade because of the inevitable plant-model mismatch. To improve the performance of these IMC-tuned PI-controllers, re-tuning of these controllers based on a more precise model of the process is necessary. The experimental tests are carried out for the cases of set-point tracking and disturbance rejection. In both cases, the successful adaptability of the RL-based PI-controller is clearly evident. Finally, in the case of a disturbance entering the process, the performance of the proposed model-free self-tuning PI-controller degrades more, when compared to the existing IMC controllers. However, the adaptability of the RL-based PI- controller provides a good solution to this problem. After being trained to handle disturbances in the process, an improved control policy is obtained, which is able to successfully return the output to the set-point. / Process Control

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Zugl.: Freiburg (Breisgau), Univ., Diss., 2005