A new approach was developed to determine the operational stability margin of a laboratory scale combustor. Applying modern and robust techniques and tools from Dynamical System Theory, the approach was based on three basic steps. In the first step, a gray-box thermoacoustical model for the combustor was derived. The second step consisted in applying System Identification techniques to experimental data in order to validate the model and estimate its parameters. The application of these techniques to experimental data under different operating conditions allowed us to determine the functional dependence of the model parameters upon changes in an experimental control parameter. Finally, the third step consisted in using that functional dependence to predict the response of the system at different operating conditions and, ultimately, estimate its operational stability margin. The results indicated that a low-order stochastic non-linear model, including two excited modes, has been identified and the combustor operational stability margin could be estimated by applying a continuation method.

Locomotion is an integral part of a whole range of animal behaviours. The basic rhythm for locomotion in vertebrates has been shown to arise from local networks residing in the spinal cord and these networks are known as central pattern generators (CPG). However, during the locomotion, these centres are constantly interacting with the sensory feedback signals coming from muscles, joints and peripheral skin receptors in order to adapt the stepping or swimming to varying environmental conditions. Conceptual models of vertebrate locomotion have been constructed using mathematical models of locomotor subsystems based on the neurophysiological evidence obtained primarily in the cat and the salamander, an amphibian with a sprawling posture. Such models provide opportunity for studying the key elements in the transition from aquatic to terrestrial locomotion. Several aspects of locomotor control using the cat or the salamander as an animal model have been investigated employing computer simulations and here we use the same approach to address a number of questions or/and hypotheses related to rhythmic locomotion in quadrupeds. Some of the involved questions are, the role of mechanical linkage during deafferented walking, finding inherent stabilities/instabilities of muscle-joint interactions during normal walking and estimating phase dependent controlability of muscle action over joints. Also we investigate limb and body coordination for different gaits, use of side-stepping in front limbs for turning and the role of sensory feedback in gait generation and transitions in salamanders.      This thesis presents the basics of the biologically realistic models of cat and salamander locomotion and summarizes computational methods in modeling quadruped locomotor subsystems such as CPG, limb muscles and sensory pathways. In the case of cat hind limb, we conclude that the mechanical linkages between the legs play a major role in producing the alternating gait. In another experiment we use the model to identify open-loop linear transfer functions between muscle activations and joint angles while ongoing locomotion. We hypothesize that the musculo-skeletal system for locomotion in animals, at least in cats, operates under critically damped condition.      The 3D model of the salamander is successfully used to mimic locomotion on level ground and in water. We compare the walking gait with the trotting gait in simulations. We also found that for turning, the use of side-stepping alone or in combination with trunk bending is more effective than the use of trunk bending alone. The same model together with a more realistic CPG composed of spiking neurons was used to investigate the role of sensory feedback in gait generation and transition. We found that the proprioceptive sensory inputs are essential in obtaining the walking gait, whereas the trotting gait is more under central (CPG) influence compared to that of the peripheral or sensory feedback.      This thesis work sheds light on understanding the neural control mechanisms behind vertebrate locomotion. Additionally, both neuro-mechanical models can be used for further investigations in finding new control algorithms which give robust, adaptive, efficient and realistic stepping in each leg, which would be advantageous since it can be implemented on a controller of a quadruped-robotic device. / This work is Funded by Swedish International Development cooperation Agency (SIDA). QC 20111110

Consider a Gaussian stationary stochastic vector process with the property that designated pairs of components are conditionally independent given the rest of the components. Such processes can be represented on a graph where the components are nodes and the lack of a connecting link between two nodes signifies conditional independence. This leads to a sparsity pattern in the inverse of the matrix-valued spectral density. Such graphical models find applications in speech, bioinformatics, image processing, econometrics and many other fields, where the problem to fit an autoregressive (AR) model to such a process has been considered. In this paper we take this problem one step further, namely to fit an autoregressive moving-average (ARMA) model to the same data. We develop a theoretical framework and an optimization procedure which also spreads further light on previous approaches and results. This procedure is then applied to the identification problem of estimating the ARMA parameters as well as the topology of the graph from statistical data. / <p>Updated from "Preprint" to "Article" QC 20130627</p>

Research Doctorate - Doctor of Philosophy (PhD) / Mechanical systems experience undesirable vibration in response to environmental and operational forces. Slight vibrations can limit the accuracy of sensitive instruments or cause error in micro- and nano-manufacturing processes. Larger vibrations, as experienced by load bearing structures, can cause fatigue and contribute to mechanical failure. The suppression of vibration is a necessity in many scientific and engineering applications. Piezoelectric and electromagnetic transducers have been employed in countless applications as sensors, actuators, or both. In cases where traditional passive mechanical vibration control is inadequate, piezoelectric and electromagnetic actuators have been used within feedback control systems to suppress vibration. A counter-active force is applied in response to a measured vibration. In this work, a new approach to the control of mechanical vibration is introduced. By presenting an appropriately designed electrical impedance to the terminals of a piezoelectric or electromagnetic transducer, vibration in the host structure can be suppressed. Standard LQG, H2, and H∞ synthesis techniques are employed to facilitate the design of optimal shunt impedances. No feedback sensor or auxiliary transducer is required. Vibration control problems are typically based on the minimization of displacement or velocity at a single point. For spatially distributed systems, such as aircraft wings, any single point may not suitably represent the overall structural vibration. Spatial system identification is introduced as a method for procuring global models of flexible structures. Spatial models can be used to properly specify the performance objective of an active vibration control system. Experimental results are presented throughout to clarify and validate the concepts presented.

Diss. Uppsala : Univ., 2004.

Diss. (sammanfattning) Uppsala : Univ., 2004. / Härtill 8 uppsatser.

Thesis (Ph.D.)--University of Wollongong, 2007. / Typescript. Includes bibliographical references: p. 172-198.

Thesis (M.Eng.(Control Engineering))--University of Pretoria, 2008. / Summary in English. Includes bibliographical references.

In this thesis a quadrotor is studied and a linear model is derived using grey-box estimation, a discipline in system identification where a model structure based on physical relations is used and the parameters are estimated using input-output measurements. From IMU measurements and measured PWM signals to the four motors, a direct approach using the prediction-error method is applied. To investigate the impact of the unknown controller the two-stage method, a closed-loop approach in system identification,  is applied as well. The direct approach was enough for estimating the model parameters. The resulting model manages to simulate the major dynamics for the vertical acceleration and the angular rates well enough  for future control design.

A radio base station (RBS) may become overloaded if too many mobile devices communicate with it at the same time. This could happen at for instance sport events or in the case of accidents. To prevent CPU overload, the RBS is provided with a controller that adjusts the acceptance rate, the maximum number of connection requests that can be accepted per time interval. The current controller is tuned in real radio base stations and the procedure is both time consuming and expensive. This, combined with the fact that the mobile data usage is predicted to increase puts more pressure on today's system. Thus, there is a need to be able to simulate the system in order to suggest an alternative controller. In this thesis, an implementation of the system is developed in Matlab in order to simulate the RBS system load control behaviour. A CPU load model is estimated using system identification. The current version of the CPU load controller and an alternative PI CPU load controller are implemented. Both are evaluated on different test cases and this shows that it is possible to increase the performance of the system with the alternative CPU load controller, both in terms of lower amount of rejected connection requests and decreased CPU load overshoot.