AMB system for high-speed motors using automatic commissioning

The general trend towards increasing e ciency and energy density drives the industry to high-speed technologies. Active Magnetic Bearings (AMBs) are one of the technologies that allow contactless support of a rotating body. Theoretically, there are no limitations on the rotational speed. The absence of friction, low maintenance cost, micrometer precision, and programmable sti ness have made AMBs a viable choice for highdemanding applications. Along with the advances in power electronics, such as signi cantly improved reliability and cost, AMB systems have gained a wide adoption in the industry. The AMB system is a complex, open-loop unstable system with multiple inputs and outputs. For normal operation, such a system requires a feedback control. To meet the high demands for performance and robustness, model-based control techniques should be applied. These techniques require an accurate plant model description and uncertainty estimations. The advanced control methods require more e ort at the commissioning stage. In this work, a methodology is developed for an automatic commissioning of a subcritical, rigid gas blower machine. The commissioning process includes open-loop tuning of separate parts such as sensors and actuators. The next step is to apply a system identi cation procedure to obtain a model for the controller synthesis. Finally, a robust model-based controller is synthesized and experimentally evaluated in the full operating range of the system. The commissioning procedure is developed by applying only the system components available and a priori knowledge without any additional hardware. Thus, the work provides an intelligent system with a self-diagnostics feature and an automatic commissioning.

Towards combining interactive mobile TV and smart spaces : architectures, tools and application development

Technological developments in microprocessors and ICT landscape have made a shift to a new era where computing power is embedded in numerous small distributed objects and devices in our everyday lives. These small computing devices are ne-tuned to perform a particular task and are increasingly reaching our society at every level. For example, home appliances such as programmable washing machines, microwave ovens etc., employ several sensors to improve performance and convenience. Similarly, cars have on-board computers that use information from many di erent sensors to control things such as fuel injectors, spark plug etc., to perform their tasks e ciently. These individual devices make life easy by helping in taking decisions and removing the burden from their users. All these objects and devices obtain some piece of information about the physical environment. Each of these devices is an island with no proper connectivity and information sharing between each other. Sharing of information between these heterogeneous devices could enable a whole new universe of innovative and intelligent applications. The information sharing between the devices is a diffcult task due to the heterogeneity and interoperability of devices. Smart Space vision is to overcome these issues of heterogeneity and interoperability so that the devices can understand each other and utilize services of each other by information sharing. This enables innovative local mashup applications based on shared data between heterogeneous devices. Smart homes are one such example of Smart Spaces which facilitate to bring the health care system to the patient, by intelligent interconnection of resources and their collective behavior, as opposed to bringing the patient into the health system. In addition, the use of mobile handheld devices has risen at a tremendous rate during the last few years and they have become an essential part of everyday life. Mobile phones o er a wide range of different services to their users including text and multimedia messages, Internet, audio, video, email applications and most recently TV services. The interactive TV provides a variety of applications for the viewers. The combination of interactive TV and the Smart Spaces could give innovative applications that are personalized, context-aware, ubiquitous and intelligent by enabling heterogeneous systems to collaborate each other by sharing information between them. There are many challenges in designing the frameworks and application development tools for rapid and easy development of these applications. The research work presented in this thesis addresses these issues. The original publications presented in the second part of this thesis propose architectures and methodologies for interactive and context-aware applications, and tools for the development of these applications. We demonstrated the suitability of our ontology-driven application development tools and rule basedapproach for the development of dynamic, context-aware ubiquitous iTV applications.

Neural mechanisms underlying conscious and unconscious vision. Evidence from event-related potentials and transcranial magnetic stimulation

Vision affords us with the ability to consciously see, and use this information in our behavior. While research has produced a detailed account of the function of the visual system, the neural processes that underlie conscious vision are still debated. One of the aims of the present thesis was to examine the time-course of the neuroelectrical processes that correlate with conscious vision. The second aim was to study the neural basis of unconscious vision, that is, situations where a stimulus that is not consciously perceived nevertheless influences behavior. According to current prevalent models of conscious vision, the activation of visual cortical areas is not, as such, sufficient for consciousness to emerge, although it might be sufficient for unconscious vision. Conscious vision is assumed to require reciprocal communication between cortical areas, but views differ substantially on the extent of this recurrent communication. Visual consciousness has been proposed to emerge from recurrent neural interactions within the visual system, while other models claim that more widespread cortical activation is needed for consciousness. Studies I-III compared models of conscious vision by studying event-related potentials (ERP). ERPs represent the brain’s average electrical response to stimulation. The results support the model that associates conscious vision with activity localized in the ventral visual cortex. The timing of this activity corresponds to an intermediate stage in visual processing. Earlier stages of visual processing may influence what becomes conscious, although these processes do not directly enable visual consciousness. Late processing stages, when more widespread cortical areas are activated, reflect the access to and manipulation of contents of consciousness. Studies IV and V concentrated on unconscious vision. By using transcranial magnetic stimulation (TMS) we show that when early visual cortical processing is disturbed so that subjects fail to consciously perceive visual stimuli, they may nevertheless guess (above chance-level) the location where the visual stimuli were presented. However, the results also suggest that in a similar situation, early visual cortex is necessary for both conscious and unconscious perception of chromatic information (i.e. color). Chromatic information that remains unconscious may influence behavioral responses when activity in visual cortex is not disturbed by TMS. Our results support the view that early stimulus-driven (feedforward) activation may be sufficient for unconscious processing. In conclusion, the results of this thesis support the view that conscious vision is enabled by a series of processing stages. The processes that most closely correlate with conscious vision take place in the ventral visual cortex ~200 ms after stimulus presentation, although preceding time-periods and contributions from other cortical areas such as the parietal cortex are also indispensable. Unconscious vision relies on intact early visual activation, although the location of visual stimulus may be unconsciously resolved even when activity in the early visual cortex is interfered with.

Machine vision methods for process measurements in pulping

The papermaking industry has been continuously developing intelligent solutions to characterize the raw materials it uses, to control the manufacturing process in a robust way, and to guarantee the desired quality of the end product. Based on the much improved imaging techniques and image-based analysis methods, it has become possible to look inside the manufacturing pipeline and propose more effective alternatives to human expertise. This study is focused on the development of image analyses methods for the pulping process of papermaking. Pulping starts with wood disintegration and forming the fiber suspension that is subsequently bleached, mixed with additives and chemicals, and finally dried and shipped to the papermaking mills. At each stage of the process it is important to analyze the properties of the raw material to guarantee the product quality. In order to evaluate properties of fibers, the main component of the pulp suspension, a framework for fiber characterization based on microscopic images is proposed in this thesis as the first contribution. The framework allows computation of fiber length and curl index correlating well with the ground truth values. The bubble detection method, the second contribution, was developed in order to estimate the gas volume at the delignification stage of the pulping process based on high-resolution in-line imaging. The gas volume was estimated accurately and the solution enabled just-in-time process termination whereas the accurate estimation of bubble size categories still remained challenging. As the third contribution of the study, optical flow computation was studied and the methods were successfully applied to pulp flow velocity estimation based on double-exposed images. Finally, a framework for classifying dirt particles in dried pulp sheets, including the semisynthetic ground truth generation, feature selection, and performance comparison of the state-of-the-art classification techniques, was proposed as the fourth contribution. The framework was successfully tested on the semisynthetic and real-world pulp sheet images. These four contributions assist in developing an integrated factory-level vision-based process control.

Flexible multibody dynamics and intelligent control of a hydraulically driven hybrid redundant robot machine

The assembly and maintenance of the International Thermonuclear Experimental Reactor (ITER) vacuum vessel (VV) is highly challenging since the tasks performed by the robot involve welding, material handling, and machine cutting from inside the VV. The VV is made of stainless steel, which has poor machinability and tends to work harden very rapidly, and all the machining operations need to be carried out from inside of the ITER VV. A general industrial robot cannot be used due to its poor stiffness in the heavy duty machining process, and this will cause many problems, such as poor surface quality, tool damage, low accuracy. Therefore, one of the most suitable options should be a light weight mobile robot which is able to move around inside of the VV and perform different machining tasks by replacing different cutting tools. Reducing the mass of the robot manipulators offers many advantages: reduced material costs, reduced power consumption, the possibility of using smaller actuators, and a higher payload-to-robot weight ratio. Offsetting these advantages, the lighter weight robot is more flexible, which makes it more difficult to control. To achieve good machining surface quality, the tracking of the end effector must be accurate, and an accurate model for a more flexible robot must be constructed. This thesis studies the dynamics and control of a 10 degree-of-freedom (DOF) redundant hybrid robot (4-DOF serial mechanism and 6-DOF 6-UPS hexapod parallel mechanisms) hydraulically driven with flexible rods under the influence of machining forces. Firstly, the flexibility of the bodies is described using the floating frame of reference method (FFRF). A finite element model (FEM) provided the Craig-Bampton (CB) modes needed for the FFRF. A dynamic model of the system of six closed loop mechanisms was assembled using the constrained Lagrange equations and the Lagrange multiplier method. Subsequently, the reaction forces between the parallel and serial parts were used to study the dynamics of the serial robot. A PID control based on position predictions was implemented independently to control the hydraulic cylinders of the robot. Secondly, in machining, to achieve greater end effector trajectory tracking accuracy for surface quality, a robust control of the actuators for the flexible link has to be deduced. This thesis investigates the intelligent control of a hydraulically driven parallel robot part based on the dynamic model and two schemes of intelligent control for a hydraulically driven parallel mechanism based on the dynamic model: (1) a fuzzy-PID self-tuning controller composed of the conventional PID control and with fuzzy logic, and (2) adaptive neuro-fuzzy inference system-PID (ANFIS-PID) self-tuning of the gains of the PID controller, which are implemented independently to control each hydraulic cylinder of the parallel mechanism based on rod length predictions. The serial component of the hybrid robot can be analyzed using the equilibrium of reaction forces at the universal joint connections of the hexa-element. To achieve precise positional control of the end effector for maximum precision machining, the hydraulic cylinder should be controlled to hold the hexa-element. Thirdly, a finite element approach of multibody systems using the Special Euclidean group SE(3) framework is presented for a parallel mechanism with flexible piston rods under the influence of machining forces. The flexibility of the bodies is described using the nonlinear interpolation method with an exponential map. The equations of motion take the form of a differential algebraic equation on a Lie group, which is solved using a Lie group time integration scheme. The method relies on the local description of motions, so that it provides a singularity-free formulation, and no parameterization of the nodal variables needs to be introduced. The flexible slider constraint is formulated using a Lie group and used for modeling a flexible rod sliding inside a cylinder. The dynamic model of the system of six closed loop mechanisms was assembled using Hamilton’s principle and the Lagrange multiplier method. A linearized hydraulic control system based on rod length predictions was implemented independently to control the hydraulic cylinders. Consequently, the results of the simulations demonstrating the behavior of the robot machine are presented for each case study. In conclusion, this thesis studies the dynamic analysis of a special hybrid (serialparallel) robot for the above-mentioned special task involving the ITER and investigates different control algorithms that can significantly improve machining performance. These analyses and results provide valuable insight into the design and control of the parallel robot with flexible rods.

A Mobile High Sensitivity On-field Organophosphorous Compounds Detecting System for IoT Based Food Safety Tracking

Food safety has always been a social issue that draws great public attention. With the rapid development of wireless communication technologies and intelligent devices, more and more Internet of Things (IoT) systems are applied in the food safety tracking field. However, connection between things and information system is usually established by pre-storing information of things into RFID Tag, which is inapplicable for on-field food safety detection. Therefore, considering pesticide residue is one of the severe threaten to food safety, a new portable, high-sensitivity, low-power, on-field organophosphorus (OP) compounds detection system is proposed in this thesis to realize the on-field food safety detection. The system is designed based on optical detection method by using a customized photo-detection sensor. A Micro Controller Unit (MCU) and a Bluetooth Low Energy (BLE) module are used to quantize and transmit detection result. An Android Application (APP) is also developed for the system to processing and display detection result as well as control the detection process. Besides, a quartzose sample container and black system box are also designed and made for the system demonstration. Several optimizations are made in wireless communication, circuit layout, Android APP and industrial design to realize the mobility, low power and intelligence.