Mechatronics of systems with undetermined configurations

This work is submitted for the award of a PhD by published works. It deals with some of the efforts of the author over the last ten years in the field of Mechatronics. Mechatronics is a new area invented by the Japanese in the late 1970's, it consists of a synthesis of computers and electronics to improve mechanical systems. To control any mechanical event three fundamental features must be brought together: the sensors used to observe the process, the control software, including the control algorithm used and thirdly the actuator that provides the stimulus to achieve the end result. Simulation, which plays such an important part in the Mechatronics process, is used in both in continuous and discrete forms. The author has spent some considerable time developing skills in all these areas. The author was certainly the first at Middlesex to appreciate the new developments in Mechatronics and their significance for manufacturing. The author was one of the first mechanical engineers to recognise the significance of the new transputer chip. This was applied to the LQG optimal control of a cinefilm copying process. A 300% improvement in operating speed was achieved, together with tension control. To make more efficient use of robots they have to be made both faster and cheaper. The author found extremely low natural frequencies of vibration, ranging from 3 to 25 Hz. This limits the speed of response of existing robots. The vibration data was some of the earliest available in this field, certainly in the UK. Several schemes have been devised to control the flexible robot and maintain the required precision. Actuator technology is one area where mechatronic systems have been the subject of intense development. At Middlesex we have improved on the Aexator pneumatic muscle actuator, enabling it to be used with a precision of about 2 mm. New control challenges have been undertaken now in the field of machine tool chatter and the prevention of slip. A variety of novel and traditional control algorithms have been investigated in order to find out the best approach to solve this problem

Graduate School of Engineering and Management Catalog 2018-2019

The Graduate Catalog represents the offerings, programs, and requirements in effect at the time of publication

Biometric features modeling to measure students engagement.

The ability to measure students’ engagement in an educational setting may improve student retention and academic success, revealing which students are disinterested, or which segments of a lesson are causing difficulties. This ability will facilitate timely intervention in both the learning and the teaching process in a variety of classroom settings. In this dissertation, an automatic students engagement measure is proposed through investigating three main engagement components of the engagement: the behavioural engagement, the emotional engagement and the cognitive engagement. The main goal of the proposed technology is to provide the instructors with a tool that could help them estimating both the average class engagement level and the individuals engagement levels while they give the lecture in real-time. Such system could help the instructors to take actions to improve students\u27 engagement. Also, it can be used by the instructor to tailor the presentation of material in class, identify course material that engages and disengages with students, and identify students who are engaged or disengaged and at risk of failure. A biometric sensor network (BSN) is designed to capture data consist of individuals facial capture cameras, wall-mounted cameras and high performance computing machine to capture students head pose, eye gaze, body pose, body movements, and facial expressions. These low level features will be used to train a machine-learning model to estimate the behavioural and emotional engagements in either e-learning or in-class environment. A set of experiments is conducted to compare the proposed technology with the state-of-the-art frameworks in terms of performance. The proposed framework shows better accuracy in estimating both behavioral and emotional engagement. Also, it offers superior flexibility to work in any educational environment. Further, this approach allows quantitative comparison of teaching methods, such as lecture, flipped classrooms, classroom response systems, etc. such that an objective metric can be used for teaching evaluation with immediate closed-loop feedback to the instructor

Proceedings of the 2nd Annual Conference on NASA/University Advanced Space Design Program

Topics discussed include: lunar transportation system, Mars rover, lunar fiberglass production, geosynchronous space stations, regenerative system for growing plants, lunar mining devices, lunar oxygen transporation system, mobile remote manipulator system, Mars exploration, launch/landing facility for a lunar base, and multi-megawatt nuclear power system

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

A framework based on Gaussian mixture models and Kalman filters for the segmentation and tracking of anomalous events in shipboard video

Anomalous indications in monitoring equipment on board U.S. Navy vessels must be handled in a timely manner to prevent catastrophic system failure. The development of sensor data analysis techniques to assist a ship\u27s crew in monitoring machinery and summon required ship-to-shore assistance is of considerable benefit to the Navy. In addition, the Navy has a large interest in the development of distance support technology in its ongoing efforts to reduce manning on ships. In this thesis, algorithms have been developed for the detection of anomalous events that can be identified from the analysis of monochromatic stationary ship surveillance video streams. The specific anomalies that we have focused on are the presence and growth of smoke and fire events inside the frames of the video stream. The algorithm consists of the following steps. First, a foreground segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced using morphological operations. Following this step, the anomalous indication is tracked between frames using Kalman filtering. Finally, gray level shape and motion features corresponding to the anomaly are subjected to principal component analysis and classified using a multilayer perceptron neural network. The algorithm is exercised on 68 video streams that include the presence of anomalous events (such as fire and smoke) and benign/nuisance events (such as humans walking the field of view). Initial results show that the algorithm is successful in detecting anomalies in video streams, and is suitable for application in shipboard environments

Hydro-meteorological data quality assurance and improvement

Advances in measurement equipment and data transfer enabled easy and economic automatic monitoring of various hydro-meteorological variables. The main characteristic of such automatic monitoring systems is that they do not rely on human activities, but only on electronic devices. Even if those electronic devices are of highest quality and accuracy, and properly tuned to specific problem, the reliability of measured values relyieson many other factors and unexpected or undesired occurrences, like modification of measurement micro location, power supply shortages or surges, etc. The sampled and acquired data values have to be additionally checked, validated and sometimes improved or cleared before further use. This paper presents an innovative approach to data validation and improvement through the framework generally applicable to all hydrological data acquisition systems. The proposed framework can incorporate any number of validation methods and can be easily customized according to the characteristics of every single measured variable. The framework allows for the self-adjustment and feedback to support self-learning of used validation methods, same as expert-controlled learning and supervision. After data validation, for low-scored data, its value quality can be improved if redundant data exist, so framework has the data reconstruction module. By applying different interpolation techniques or using redundant data value the new data is created same as accompanying metadata with the reconstruction history. After data reconstruction, the framework supports the data adjustment, the post-processing phase where the data is adjusted for the specific needs of each user. Every validated and sometimes improved data value is accompanied with a meta-data that holds its validation grade as a quality indicator for further use.

Road vehicle state estimation using low-cost GPS/INS

Due to noise and bias in the Inertial Navigation System (INS), vehicle dynamics measurements using the INS are inaccurate. Although alternative methods involving the integration of INS with accurate Global Positioning System (GPS) exist and are accurate, this kind of system is far too expensive to become value-adding to production vehicles. This thesis therefore considers two aspects: 1) the possibility of estimating vehicle dynamics using low-cost INS and GPS, and 2) the importance of vehicle dynamics in terms of handling in the eyes of customers upon vehicle purchase. The former aspect is considered from an engineering perspective and the latter is studied in a marketing context. From an engineering point of view, knowledge of vehicle dynamics not only improves existing safety control systems, such the Anti-lock Braking System (ABS) and Electronic Stabilising Program (ESP), but also allows the development of new systems. Based on modelling and simulation in MATLAB/Simulink, low-cost GPS and in-car INS (such as accelerometers, gyroscopes and wheel speed sensors) measurements are fused using Kalman Filters (KFs) to estimate the vehicle dynamics. These estimations are then compared with the simulation results from IPG Car- Maker. For most simulations, the speed of the vehicle is kept between 15 to 55kph. It is found that while triple KF designs are able to estimate the tyre radius, the longitudinal velocity and the heading angle accurately, an integrated KF design with known vehicle parameters is also able to estimate the lateral velocity precisely. Apart from studying and comparing different KF designs with restricted sensors quality, the effects and benefits of different sensor qualities in dynamic estimations are also studied via the variation of sensor sampling rates and accuracies. This investigation produces a design procedure and estimation error analyses (theoretical and graphical) which may help future engineers in designing their KFs. From a marketing perspective, it is important to understand customers’ purchase reasons in order to allocate resources more efficiently and effectively. As GPS/INS KF designs are able to enhance vehicle handling, it is vital to understand the relative importance of vehicle handling as a consumer purchase choice criterion. Based on two surveys, namely the New Vehicle Experience Survey in the US (NVES US) and the New Car Buyer Survey in the UK (NCBS UK), analyses are performed in a computer program called the Predictive Analytics SoftWare (PASW), which is formerly known as the Statistical Package for the Social Sciences (SPSS). The number of purchase reasons are first reduced with factor analysis, the latent factors produced are then used in the SPSS Two Step Cluster analysis for customer segmentation. With the customer segments and the latent factors defined, a discriminant analysis is carried out to determine customer type in the automobile sector, in particular for Jaguar Cars. It is found that customers in general take vehicle handling for granted and often underrate its importance in their purchase. New vehicle handling-aided systems therefore need to be marketed in terms of the value they add to other benefits such as reliability and performance in order to increase sales and stakeholder value

The low-level guidance of an experimental autonomous vehicle

This thesis describes the data processing and the control that constitutes a method of guidance for an autonomous guided vehicle (AGV) operating in a predefined and structured environment such as a warehouse or factory. A simple battery driven vehicle has been constructed which houses an MC68000 based microcomputer and a number of electronic interface cards. In order to provide a user interface, and in order to integrate the various aspects of the proposed guidance method, a modular software package has been developed. This, along with the research vehicle, has been used to support an experimental approach to the research. The vehicle's guidance method requires a series of concatenated curved and straight imaginary Unes to be passed to the vehicle as a representation of a planned path within its environment. Global position specifications for each line and the associated AGV direction and demand speed for each fine constitute commands which are queued and executed in sequence. In order to execute commands, the AGV is equipped with low level sensors (ultrasonic transducers and optical shaft encoders) which allow it to estimate and correct its global position continually. In addition to a queue of commands, the AGV also has a pre-programmed knowledge of the position of a number of correction boards within its environment. These are simply wooden boards approximately 25cm high and between 2 and 5 metres long with small protrusions ("notches") 4cm deep and 10cm long at regular (Im) intervals along its length. When the AGV passes such a correction board, it can measure its perpendicular distance and orientation relative to that board using two sets of its ultrasonic sensors, one set at the rear of the vehicle near to the drive wheels and one set at the front of the vehicle. Data collected as the vehicle moves parallel to a correction board is digitally filtered and subsequently a least squares line fitting procedure is adopted. As well as improving the reliability and accuracy of orientation and distance measurements relative to the board, this provides the basis for an algorithm with which to detect and measure the position of the protrusions on the correction board. Since measurements in three planar, local coordinates can be made (these are: x, the distance travelled parallel to a correction board; and y,the perpendicular distance relative to a correction board; and Ɵ, the clockwise planar orientation relative to the correction board), global position estimation can be corrected. When position corrections are made, it can be seen that they appear as step disturbances to the control system. This control system has been designed to allow the vehicle to move back onto its imaginary line after a position correction in a critically damped fashion and, in the steady state, to track both linear and curved command segments with minimum error