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Tuned mass dampers (TMDs) are auxiliary damping devices installed within tall structures to reduce undesirable wind-induced vibrations and to enhance the overall system damping and hence, the dissipative capacity. The design of TMDs involves the selection of optimal auxiliary mass, frequency, and damping, based on the main structure's mass, natural frequency and damping properties. TMDs are inherently susceptible to detuning, where the auxiliary parameters are no longer optimal due to deterioration or changes within the system, resulting in a degradation in their performance. In order to correct for this detuning, it is necessary to perform a condition assessment while the TMDs are in service. The main goal of this thesis is to present a methodology to conduct condition assessment while the TMDs are in service. The proposed methodology does not involve either restraining the TMD or providing controlled external excitation to the structure, and relies on ambient measurements only. The first phase in the condition assessment is to estimate the bare structure's modal properties using acceleration measurements obtained from the structure while the TMDs are unrestrained. The present work accomplishes this goal within the framework of parametric identification using Kalman filtering, where the unknown parameters (bare modal properties) are appended to the state vector and estimated. Unlike most of the literature on this subject, the noise statistics for the filter are not assumed to be known a priori. They are estimated from the measurements and incorporated into the filter equations. This filter involves direct feedthrough of the process noise in the measurement equation and the appropriate filter is derived and used following the noise covariance estimation step. In the next phase, criteria to assess the condition of the TMD are developed. They include optimal tuning parameters established using simulated experiments and measured equivalent viscous damping. The research considered pendulum tuned mass dampers (PTMDs), which presently account for a large fraction of full-scale applications. Results were demonstrated using numerical investigations, a bench-scale model equipped with an adaptive mechanism for adjusting auxiliary damper parameters, and a full-scale PTMD-equipped structure. The main contributions of this thesis are: (a) a broader understanding of the coupled biaxial behaviour of PTMDs has been developed; (b) a systematic procedure for estimating the underlying modal characteristics of the structure from ambient vibration measurements within the framework of Kalman filtering has been achieved; (c) a comprehensive framework to undertake condition assessment of TMDs has been presented, integrating parametric identification from measured response data and performance prediction for design period wind events using boundary layer wind tunnel studies. The work provided new insight into the design and behaviour of PTMDs and presented a comprehensive approach to quantify their performance. The Kalman filtering framework also provides an efficient platform to build adaptive passive tuned mass dampers that can be tuned in place and adjusted to correct for detuning and accommodate various operating conditions.

This thesis presents a data-driven approach to constrained control in the form of a subspace-based state-space system identification algorithm integrated into a model predictive controller. Generally this approach has been termed model-free predictive control in the literature. Previous research into this area focused on the system identification aspects resulting in an omission of many of the features that would make such a control strategy attractive to industry. These features include constraint handling, zero-offset setpoint tracking and non-stationary disturbance rejection. The link between non-stationary disturbance rejection in subspace-based state-space system identification and integral action in state-space based model predictive control was shown. Parameterization with Laguerre orthonormal functions was proposed for the reduction in computational load of the controller. Simulation studies were performed using three real-world systems demonstrating: identification capabilities in the presence of white noise and non-stationary disturbances; unconstrained and constrained control; and the benefits and costs of parameterization with Laguerre polynomials.

This thesis reports the design and control of the ??GreenWeeder??, a non-herbicidal autonomous weeding robot, in order to autonomously track crop rows for weeding through electrocution in the inter-row space. The four wheel mobile robot platform was designed and built with a motorised Ackerman steering system allowing the robot to steer up to 30 degree left and right. It was also equipped with an electronically geared rear wheel drive, a pair of stereo cameras, a SICK LMS-291 laser range finder to localize itself with respect to the crop rows, a GPS system for obtaining the robot position in the field and a long-range communication system for tele-supervision by operators. The first prototype of the robot electrocution system was also designed and constructed to ignite 22kV electrical arcs to destroy weeds. Its operation was tested in the research field of the University of Sydney and the results of this experiment were analysed to improve the efficiency of this first prototype. An improved prototype of the electrocution system was then constructed and attached to a cradle extending out at the back of the mobile robot platform. The testing of this improved prototype was conducted at Lansdowne farm, a research field of the University of Sydney. After the construction of the robot platform, the robot control was considered with the demands for robot localization with respect to crop rows, an autonomously tracking control system and a manual control mode in order to take the robot to transportation vehicles. Firstly, the robot localization was accomplished by utilizing SICK LMS-291 laser range finder sensor for the sensing and perception of the robot. Secondly, the robot control system was developed with a PID controller, a second order model of the robot system and a first order filter. The PID controller is in the standard form with the filtered derivative and the PI part being in automatic reset configuration. The second order model was identified using Matlab System Identification toolbox based on the robot kinematic analysis. The first order filter is utilized for filtering out the lateral deviations of the robot with respect to the crop rows received from the SICK laser sensor. A Simulink simulation model of the robot control system was also built in order to fine-tune PID and filter parameters. Thirdly, the manual control mode of the robot was produced. In this mode, a joystick can be attached to a notebook to wirelessly drive the robot around or it can be plugged into a USB port at the back of the robot to drive it without the notebook. After the robot control was implemented and simulated, some experiments were conducted with the robot autonomously tracking a strip of reflective tape mimicking a crop row stuck into the ground of a laboratory. Depending on distances from the row assigned to the controller, the robot tried to keep those distances away from the row. The results showed the lateral errors of the robot with respect to the row were approximately 4.5 cm which were sufficient for our current agricultural application.

Bibliography: leaves 197-206. / viii, 210 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / The objective of this work is to study some new inverse problems related to mechanical systems, typical to the theory of vibration and engineering practice. / Thesis (Ph.D.)--University of Adelaide,Dept. of Mechanical Engineering, 1997

This thesis reports the design and control of the ??GreenWeeder??, a non-herbicidal autonomous weeding robot, in order to autonomously track crop rows for weeding through electrocution in the inter-row space. The four wheel mobile robot platform was designed and built with a motorised Ackerman steering system allowing the robot to steer up to 30 degree left and right. It was also equipped with an electronically geared rear wheel drive, a pair of stereo cameras, a SICK LMS-291 laser range finder to localize itself with respect to the crop rows, a GPS system for obtaining the robot position in the field and a long-range communication system for tele-supervision by operators. The first prototype of the robot electrocution system was also designed and constructed to ignite 22kV electrical arcs to destroy weeds. Its operation was tested in the research field of the University of Sydney and the results of this experiment were analysed to improve the efficiency of this first prototype. An improved prototype of the electrocution system was then constructed and attached to a cradle extending out at the back of the mobile robot platform. The testing of this improved prototype was conducted at Lansdowne farm, a research field of the University of Sydney. After the construction of the robot platform, the robot control was considered with the demands for robot localization with respect to crop rows, an autonomously tracking control system and a manual control mode in order to take the robot to transportation vehicles. Firstly, the robot localization was accomplished by utilizing SICK LMS-291 laser range finder sensor for the sensing and perception of the robot. Secondly, the robot control system was developed with a PID controller, a second order model of the robot system and a first order filter. The PID controller is in the standard form with the filtered derivative and the PI part being in automatic reset configuration. The second order model was identified using Matlab System Identification toolbox based on the robot kinematic analysis. The first order filter is utilized for filtering out the lateral deviations of the robot with respect to the crop rows received from the SICK laser sensor. A Simulink simulation model of the robot control system was also built in order to fine-tune PID and filter parameters. Thirdly, the manual control mode of the robot was produced. In this mode, a joystick can be attached to a notebook to wirelessly drive the robot around or it can be plugged into a USB port at the back of the robot to drive it without the notebook. After the robot control was implemented and simulated, some experiments were conducted with the robot autonomously tracking a strip of reflective tape mimicking a crop row stuck into the ground of a laboratory. Depending on distances from the row assigned to the controller, the robot tried to keep those distances away from the row. The results showed the lateral errors of the robot with respect to the row were approximately 4.5 cm which were sufficient for our current agricultural application.

Thesis (Ph.D.)--McMaster University (Canada), 1990. / Source: Dissertation Abstracts International, Volume: 62-13, Section: A, page: 0000.

Thesis (Ph. D.)--University of Wyoming, 2005. / Title from PDF title page (viewed on Oct. 16, 2007). Includes bibliographical references (p. 117-120).

Thesis (M.S.)--Rochester Institute of Technology, 1993. / Typescript. Includes bibliographical references (leaves [89]-[90]).

Thesis (M.S.)--Ohio University, March, 1998. / Title from PDF t.p.