Dissertations / Theses on the topic 'Control engineering, mechatronics and robotics not elsewhere classified'

"Robot soccer provides a fertile environment for the development of artificial intelligence techniques. Robot controls require high speed lower level reactive layers as well as higher level deliberative functions. This thesis focuses on a number of aspects in the robot soccer arena. Topics covered include boundary avoidance strategies, vision detection and the application of evolutionary learning to find fuzzy controllers for the control of mobile robot. A three input, two output controller using two angles and a distance as the input and producing two wheel velocity outputs, was developed using evolutionary learning. Current wheel velocities were excluded from the input. The controller produced was a coarse control permitting only either forward or reverse facing impact with the ball. A five input controller was developed which expanded upon the three input model by including the current wheel velocities as inputs. The controller allowed both forward and reverse facing impacts with the ball. A five input hierarchical three layer model was developed to reduce the number of rules to be learnt by an evolutionary algorithm. Its performance was the same as the five input model. Fuzzy clustering of evolved paths was limited by the information available from the paths. The information was sparse in many areas and did not produce a controller that could be used to control the robots. Research was also conducted on the derivation of simple obstacle avoidance strategies for robot soccer. A new decision region method for colour detection in the UV colour map to enable better detection of the robots using an overhead vision system. Experimental observations are given." -- abstract.

The aim of this project was to develop a Real Time Code Generator for power electronic control applications with a graphical input medium from the simulation software'Simulink'. The Real Time Code Generator (RTCG) converts the block diagram model into 'C' code. The generated code is targeted to the Digital Signal Processor based controller board. The RTCG is mainly suited for power electronic control applications in real time. It gives the user a complete control over the hardware functionality and the sequence of the program. Complex control strategies can be implemented in the space of a week whereas conventional programming would have taken months to be completed. Also, the engineer need not have an indepth knowledge of 'C' programming. The RTCG was used to control a 10 kW PWM rectifier. Different types of control systems were implemented on the PWM rectifier using the RTCG. The DSI102 Digital Signal Processor based board is the controller for the RTeG. It is manufactured by dSpace GmbH. It is an independent add-on card for IBM PC's. DS1102 card contains two state-of-the-art Texas Instruments DSP processors; the TMS320C31 and the TMS320P14. The different control techniques are the predictive current control, model based control and the vector current control. Using these control techniques, the PWM rectifier was able to transfer power bidirectionally at unity power factor with a regulated DC link voltage. Briefly, the predictive current control brings the actual current equal to the demand current in one switching cycle. The vector current control is almost as good as the predictive current control but it takes approximately three switching cycles to bring the actual current equal to the demand current. The model based control removes the ripple usually present in the output of the conventional rectifier. The predictive current control or the vector current control can be used in conjunction with the model based control.

Australian fruit growers face huge losses due to the damage sustained from flying vertebrate pests such as birds and flying foxes. Growers spend hundreds of thousands of dollars in combating these pests due to the strict stipulations by the Department of Environment and Resource Management requiring all deterrent measures to be non-lethal. Some of the counter measures include canopy netting, tunnel netting, odour repellents, sounds, lights, scare guns, fruit bags and chemicals. These methods involve high capital and periodic maintenance costs and are rendered ineffective over a period of time since these pests are intelligent enough to find ways to overcome such obstacles. Thus, fruit growers are trying to find more effective methods to control the problem while trying to minimize their costs. Emerging trends in this area, particularly in the United States of America (USA), include the use of drones, automatic detection and warning systems, which in turn, can trigger a particular type of deterrent system and thereby protect crops. Infrared/laser scanning technologies has been found effective in detecting pests in small confined areas. Such technologies are usually customised to suit a particular geographical area and also the type of flying vertebrate pests being combated. This project is geared towards the development of a detection system which can effectively detect the presence of lorikeets in a lychee orchard in the Burnett region. Acoustic detection of bird species in the field environment is a challenging endeavour due to a complex mix of sound sources. Although there are several approaches to detect them, they are effective in only some particular situations. For example, infrared technology is effective in detecting pests in small confined area because of number of hardware components required while visual observation techniques require large amount of processing power. This shortcoming has prompted a study into the development of a sensor unit that can effectively detect flying vertebrate pests like lorikeets within the specified range under conditions typically found in lychee orchards. In this research, a new portable sensing device which uses a combination of acoustic sensors that can be used trigger a beacon or a sound whenever lorikeets are detected, was used. This sensor was tested during the lychee season of 2015/16 and has been found to be effective in detecting lorikeets up to 20 metres, with the detection rate ranging from 71% to 30% in the range of 2 metres to 12 metres. The detection system was customized to be effective in a specified range to detect lorikeet calls in lychee orchards. It is very cost effective and portable. Further, this is the first time that such a detection system has been used in a lychee orchard in the Wide Bay Burnett region and in wider Australia itself. These preliminary efforts possess great potential to explore the development of such devices to entail better crop management practices in the region.

In this thesis we investigate the control of rigid robotic manipulators using robust adaptive sliding mode tracking control. Physical state constraints are incorporated using a multiplicative penalty in a Liapunov function from which we obtain analytic control laws that drive the robot's endeffector into a desired fixed target within finite time.

Stringent regulatory requirements and modern diesel engine technologies have engaged automotive manufacturers and researchers in accurately predicting and controlling diesel engine-out emissions. As a result, engine control systems have become more complex and opaquer, increasing the development time and costs. To address this challenge, Model-based control methods are an effective way to deal with the criticality of the system study and controls. And physics-based combustion engine modeling is a key to achieve it. This thesis focuses on development and validation of a physics-based model for both engine and emissions using model-based design tools from MATLAB & Simulink. Engine model equipped with exhaust gas circulation and variable geometry turbine is adopted from the previously done work which was then integrated with the combustion and emission model that predicts the heat release rates and NO_x emission from engine. Combustion model is designed based on the mass fraction burnt from CA10 to CA90 and then NO_x predicted using the extended Zeldovich mechanism. The engine models are tuned for both steady state and dynamics test points to account for engine operating range from the performance data. Various engine and combustion parameters are estimated using parameter estimation toolbox from MATLAB and Simulink by applying least squared solver to minimize the error between measured and estimated variables. This model is validated against the virtual engine model developed in GT-power for Cummins 6.7L turbo diesel engine. To account the harmonization of the testing cycles to save engine development time globally, a world harmonized stationary cycle (WHSC) is used for the validation. Sub-systems are validated individually as well as in loop with a complete model for WHSC. Engine model validation showed promising accuracy of more than 88.4 percent in average for the desired parameters required for the NO_x prediction. NO_x estimation is accurate for the cycle except warm up and cool down phase. However, NO_x prediction during these phases is limited due to actual NO_x measured data for tuning the model for real time NO_x estimation. Results are summarized at the end to compare the trend of NO_x estimation from the developed combustion and emission model to show the accuracy of in-cylinder parameters and required for the NO_x estimation.

It is essential for a mobile robot during autonomous navigation to be able to detect revisited places or loop closures while performing Vision Simultaneous Localization And Mapping (VSLAM). Loop closing has been identified as one of the critical data association problem when building maps. It is an efficient way to eliminate errors and improve the accuracy of the robot localization and mapping. In order to solve loop closing problem, the ORB-SLAM algorithm, a feature based simultaneous localization and mapping system that operates in real time is used. This system includes loop closing and relocalization and allows automatic initialization.

In order to check the performance of the algorithm, the monocular and stereo and RGB-D cameras are used. The aim of this thesis is to show the accuracy of relocalization and loop closing process using ORB SLAM algorithm in a variety of environmental settings. The performance of relocalization and loop closing in different challenging indoor scenarios are demonstrated by conducting various experiments. Experimental results show the applicability of the approach in real time application like autonomous navigation.

Soft materials have gained increasing prominence in science and technology over the last few decades. This shift from traditional rigid materials to soft, compliant materials have led to the emergence of a new class of devices which can interact with humans safely, as well as reduce the disparity in mechanical compliance at the interface of soft human tissue and rigid devices.

One of the largest application of soft materials has been in the field of flexible electronics, especially in wearable sensors. While wearable sensors for physical attributes such as strain, temperature, etc. have been popular, they lack applications and significance from a healthcare perspective. Point-of-care (POC) devices, on the other hand, provide exceptional healthcare value, bringing useful diagnostic tests to the bedside of the patient. POC devices, however, have been developed for only a limited number of health attributes. In this dissertation I propose and demonstrate wireless, wearable POC devices to measure and communicate the level of various analytes in and the properties of multiple biofluids: blood, urine, wound exudate, and sweat.

Along with sensors, another prominent area of soft materials application has been in actuators and robots which mimic biological systems not only in their action but also in their soft structure and actuation mechanisms. In this dissertation I develop design strategies to improve upon current soft robots by programming the storage of elastic strain energy. This strategy enables us to fabricate soft actuators capable of programmable and low energy consuming, yet high speed motion. Collectively, this dissertation demonstrates the use of soft compliant materials as the foundation for developing new sensors and actuators for human use and interaction.

The miniaturization and thinning of wearable, soft robotics and medical devices are soon to require higher performance modeling as the physical flexibility causes direct impacts on the electrical characteristics of the circuit – changing its behavior. As a representative flexible electronic component, the organic field effect transistor (OFET) has attracted much attention in its manufacturing as well as applications. However, as the strain and stress effects are integrated into multiphysics modelers with deeper interactions, the computational complexity and accuracy of OFET modeling is resurfacing as a limiting bottleneck.

The dissertation was organized into three interrelated studies. In the first study, the Mass-Spring-Damper (MSD) model for an inverted staggered thin film transistor (TFT) was proposed to investigate the TFT’s internal stress/strain fields, and the strain effects on the overall characteristics of the TFT. A comparison study with the finite element analysis (FEA) model shows that the MSD model can reduce memory usage and raises the computational convergence speed for rendering the same results as the FEA. The second study developed the generalized solid-state model by incorporating the density of trap states in the band structure of organic semiconductors (OSCs). The introduction of trap states allows the generalized solid-state model to describe the electrical characteristics of both inorganic TFTs and organic field-effect transistors (OFETs). It is revealed through experimental verification that the generalized solid-state model can accurately characterize the bending induced electrical properties of an OFET in the linear and saturation regimes. The third study aims to model the transient and steady-state dynamics of an arbitrary organic semiconductor device under mechanical strain. In this study, the fractional drift-diffusion (Fr-DD) model and its computational scheme with high accuracy and high convergence rate were proposed. Based on simulation and experimental validation, the transconductance and output characteristics of a bendable OFET were found to be well determined by the Fr-DD model not only in the linear and saturation regimes, but also in the subthreshold regime.

Distributed optimization over multi-agent networks has become an increasingly popular research topic as it incorporates many applications from various areas such as consensus optimization, distributed control, network resource allocation, large scale machine learning, etc. Parallel distributed solution algorithms are highly desirable as they are more scalable, more robust against agent failure, align more naturally with either underlying agent network topology or big-data parallel computing framework. In this dissertation, we consider a multi-agent optimization formulation where the global objective function is the summation of individual local objective functions with respect to local agents' decision variables of different dimensions, and the constraints include both local private constraints and shared coupling constraints. Employing and extending tools from the monotone operator theory (including resolvent operator, operator splitting, etc.) and fixed point iteration of nonexpansive, averaged operators, a series of distributed solution approaches are proposed, which are all iterative algorithms that rely on parallel agent level local updates and inter-agent coordination. Some of the algorithms require synchronizations across all agents for information exchange during each iteration while others allow asynchrony and delays. The algorithms' convergence to an optimal solution if one exists are established by first characterizing them as fixed point iterations of certain averaged operators under certain carefully designed norms, then showing that the fixed point sets of these averaged operators are exactly the optimal solution set of the original multi-agent optimization problem. The effectiveness and performances of the proposed algorithms are demonstrated and compared through several numerical examples.