The thesis presents comparative study of modern control methods for regulating under-actuates systems. The classical pendulum on a cart system, a 2-DOF under-actuated system was utilized as a benchmark system for analyzing controllers' performances. The work includes analysis, controller design and swing-up control simulation for the following three methods: Nonlinear state-space control The mathematical model based on the Lie theoretic approach with nonlinear output injection was developed. The controller accomplishes both swing-up and stabilization of the pendulum. The feasibility of the implementation is limited due to physical constraints (length of the cart rail). Fuzzy logic control A Sugeno type fuzzy inference engine implementing the control strategy was built over the system fuzzy model. The simulations showed successful controller behavior but the generated system control input exhibited some non-smoothness that could cause increase on the actuator demand. Method of embedded artificial constraints (EAC). The analysis reveals dependence between the pendulum motion and the cart acceleration. Using this artificial constraint a state link was developed and the nonlinear control problem was reduced to a linear controller design. A stabilizing linear state-space controller has been developed and methods for arbitrary pole placement and optimal linear quadratic regulator design were compared. A reduced order current estimator for velocity estimation has been studied and implemented. A software client/server controller application running on a QNX Neutrino 6.1 platform was developed. The real-time experiments conducted with the EAC/linear state-space controller confirmed good swing-up and stabilization system performance.

Whether it is a car, a mobile phone, or a computer, we are noticing how automation and production with robots plays an important role in the industry of our modern world. We find it in factories, manufacturing products, automotive cruise control, construction equipment, autopilot on airplanes, and countless other industrial applications.         Automation technology can vary greatly depending on the field of application. On one end, we have systems that are operated by the user and rely fully on human ability. Examples of these are heavy-mobile equipment, remote controlled systems, helicopters, and many more. On the other end, we have autonomous systems that are able to make algorithmic decisions independently of the user.         Society has always envisioned robots with the full capabilities of humans. However, we should envision applications that will help us increase productivity and improve our quality of life through human-robot collaboration. The questions we should be asking are: “What tasks should be automated?'', and “How can we combine the best of both humans and automation?”. This thinking leads to the idea of developing systems with some level of autonomy, where the intelligence is shared between the user and the system. Reasonably, the computerized intelligence and decision making would be designed according to mathematical algorithms and control rules.         This thesis considers these topics and shows the importance of fundamental mathematics and control design to develop automated systems that can execute desired tasks. All of this work is based on some of the most modern concepts in the subjects of robotics and control, which are synthesized by a method known as the Virtual Holonomic Constraints Approach. This method has been useful to tackle some of the most complex problems of nonlinear control, and has enabled the possibility to approach challenging academic and industrial problems. This thesis shows concepts of system modeling, control design, motion analysis, motion planning, and many other interesting subjects, which can be treated effectively through analytical methods. The use of mathematical approaches allows performing computer simulations that also lead to direct practical implementations.

This study investigates the use of five broad accountability mechanisms by gathering the perceptions of charities involved in the Canadian effort to reduce under-5 mortality abroad. While annual deaths in children under the age of 5 declined from an estimate of over 24.0 million in 1960 to under 8.0 million in 2010, mortality reduction goals have been established and missed for decades. As worldwide economies worsen, the amount of funds available for development assistance can be expected to decrease. This study seeks to determine if having accountability mechanisms is perceived to improve organizational behaviour, results and/or reduce costs. It uses a mixed methods approach including: a literature review to gain an understanding of accountability, effectiveness, development and under-5 mortality; key informant interviews to gain an understanding of funders, charities and development; a survey to gather the information required to answer the research questions; and a multiple-case study to gain a better appreciation of how accountability is used and to gather evidence of survey responses. The study investigates: which accountability mechanisms charities have, why they have them and the associated accountability holders; standards body memberships; the relationship between accountability mechanisms and various organizational characteristics; and the perceived effects of accountability mechanisms on organizational behaviour, results and costs. The survey finds that: charities say that they adopt accountability mechanisms because it is a good management practice that is perceived to improve organizational behaviour and results while not incurring costs in excess of the benefits; charities are more likely to adopt accountability mechanisms due to internal pressures than external pressures; the use of accountability mechanisms increases with organization size; and there is a greater difference in use of accountability mechanisms between small and large charities than there is between medium and large charities. The multiple-case study confirms the survey results. This study fills a gap in the literature by providing a Canadian perspective on the use of accountability mechanisms and the relationships amongst them and their perceived effects on organizational behaviour, results and costs. As economic burdens increase, increased accountability may lead to improved results even with fewer dollars.

This study investigates the use of five broad accountability mechanisms by gathering the perceptions of charities involved in the Canadian effort to reduce under-5 mortality abroad. While annual deaths in children under the age of 5 declined from an estimate of over 24.0 million in 1960 to under 8.0 million in 2010, mortality reduction goals have been established and missed for decades. As worldwide economies worsen, the amount of funds available for development assistance can be expected to decrease. This study seeks to determine if having accountability mechanisms is perceived to improve organizational behaviour, results and/or reduce costs. It uses a mixed methods approach including: a literature review to gain an understanding of accountability, effectiveness, development and under-5 mortality; key informant interviews to gain an understanding of funders, charities and development; a survey to gather the information required to answer the research questions; and a multiple-case study to gain a better appreciation of how accountability is used and to gather evidence of survey responses. The study investigates: which accountability mechanisms charities have, why they have them and the associated accountability holders; standards body memberships; the relationship between accountability mechanisms and various organizational characteristics; and the perceived effects of accountability mechanisms on organizational behaviour, results and costs. The survey finds that: charities say that they adopt accountability mechanisms because it is a good management practice that is perceived to improve organizational behaviour and results while not incurring costs in excess of the benefits; charities are more likely to adopt accountability mechanisms due to internal pressures than external pressures; the use of accountability mechanisms increases with organization size; and there is a greater difference in use of accountability mechanisms between small and large charities than there is between medium and large charities. The multiple-case study confirms the survey results. This study fills a gap in the literature by providing a Canadian perspective on the use of accountability mechanisms and the relationships amongst them and their perceived effects on organizational behaviour, results and costs. As economic burdens increase, increased accountability may lead to improved results even with fewer dollars.

The collection of crop yield data has become much easier with the introduction of technologies such as the Global Positioning System (GPS), ground-based yield sensors, and Geographic Information Systems (GIS). This explosive growth and widespread use of spatial data has challenged the ability to derive useful spatial knowledge. In addition, outlier detection as one important pre-processing step remains a challenge because the technique and the definition of spatial neighbourhood remain non-trivial, and the quantitative assessments of false positives, false negatives, and the concept of region outlier remain unexplored. The overall aim of this study is to evaluate different spatial outlier detection techniques in terms of their accuracy and computational efficiency, and examine the performance of these outlier removal techniques in a site-specific management context. In a simulation study, unconditional sequential Gaussian simulation is performed to generate crop yield as the response variable along with two explanatory variables. Point and region spatial outliers are added to the simulated datasets by randomly selecting observations and adding or subtracting a Gaussian error term. With simulated data which contains known spatial outliers in advance, the assessment of spatial outlier techniques can be conducted as a binary classification exercise, treating each spatial outlier detection technique as a classifier. Algorithm performance is evaluated with the area and partial area under the ROC curve up to different true positive and false positive rates. Outlier effects in on-farm research are assessed in terms of the influence of each spatial outlier technique on coefficient estimates from a spatial regression model that accounts for autocorrelation. Results indicate that for point outliers, spatial outlier techniques that account for spatial autocorrelation tend to be better than standard spatial outlier techniques in terms of higher sensitivity, lower false positive detection rate, and consistency in performance. They are also more resistant to changes in the neighbourhood definition. In terms of region outliers, standard techniques tend to be better than spatial autocorrelation techniques in all performance aspects because they are less affected by masking and swamping effects. In particular, one spatial autocorrelation technique, Averaged Difference, is superior to all other techniques in terms of both point and region outlier scenario because of its ability to incorporate spatial autocorrelation while at the same time, revealing the variation between nearest neighbours. In terms of decision-making, all algorithms led to slightly different coefficient estimates, and therefore, may result in distinct decisions for site-specific management. The results outlined here will allow an improved removal of crop yield data points that are potentially problematic. What has been determined here is the recommendation of using Averaged Difference algorithm for cleaning spatial outliers in yield dataset. Identifying the optimal nearest neighbour parameter for the neighbourhood aggregation function is still non-trivial. The recommendation is to specify a large number of nearest neighbours, large enough to capture the region size. Lastly, the unbiased coefficient estimates obtained with Average Difference suggest it is the better method for pre-processing spatial outliers in crop yield data, which underlines its suitability for detecting spatial outlier in the context of on-farm research.

In traffic safety analysis, a large number of distributions have been proposed to analyze motor vehicle crashes. Among those distributions, the traditional Poisson and Negative Binomial (NB) distributions have been the most commonly used. Although the Poisson and NB models possess desirable statistical properties, their application on modeling motor vehicle crashes are associated with limitations. In practice, traffic crash data are often over-dispersed. On rare occasions, they have shown to be under-dispersed. The over-dispersed and under-dispersed data can lead to the inconsistent standard errors of parameter estimates using the traditional Poisson distribution. Although the NB has been found to be able to model over-dispersed data, it cannot handle under-dispersed data. Among those distributions proposed to handle over-dispersed and under-dispersed datasets, the Conway-Maxwell-Poisson (COM-Poisson) and double Poisson (DP) distributions are particularly noteworthy. The DP distribution and its generalized linear model (GLM) framework has seldom been investigated and applied since its first introduction 25 years ago. The objectives of this study are to: 1) examine the applicability of the DP distribution and its regression model for analyzing crash data characterized by over- and under-dispersion, and 2) compare the performances of the DP distribution and DP GLM with those of the COM-Poisson distribution and COM-Poisson GLM in terms of goodness-of-fit (GOF) and theoretical soundness. All the DP GLMs in this study were developed based on the approximate probability mass function (PMF) of the DP distribution. Based on the simulated data, it was found that the COM-Poisson distribution performed better than the DP distribution for all nine mean-dispersion scenarios and that the DP distribution worked better for high mean scenarios independent of the type of dispersion. Using two over-dispersed empirical datasets, the results demonstrated that the DP GLM fitted the over-dispersed data almost the same as the NB model and COM-Poisson GLM. With the use of the under-dispersed empirical crash data, it was found that the overall performance of the DP GLM was much better than that of the COM-Poisson GLM in handling the under-dispersed crash data. Furthermore, it was found that the mathematics to manipulate the DP GLM was much easier than for the COM-Poisson GLM and that the DP GLM always gave smaller standard errors for the estimated coefficients.

Power systems in many countries are stressed towards their stability limit. If these stable systems experience any unexpected contingencies, or disturbances, there is a significant risk of instability, which may lead to wide-spread blackout. Existing methods to minimize the risk of stability and excessive frequency decline; need to be redeveloped to address these new challenges. This research project will develop a new emergency control methodology, which can more effectively prevent power system frequency and voltage instability under emergency conditions. Frequency and voltage instability are two major concerns in power system operation. The primary aim of this project is to develop new optimal load shedding techniques, which are able to better address various voltage and frequency instability issues for power systems emergency control purpose. In this thesis, new approach of load shedding for frequency and voltage stability are presented. For the load shedding to prevent frequency collapse, System Frequency Respond – Under Frequency Load Shedding (SFR-UFLS) from the previous approach has been redeveloped to compute an optimal load shedding scheme. The limitation of previous optimal load shed method is that they only shed load following one particular contingency event. As an improvement of this method, we developed a technique that protects against a range of contingencies. For the load shedding to prevent voltage collapse, The proposed method is then tested on the 39-bus New England test system. Generators are of different importance to the system in terms of voltage stability. It is essential to investigate generators’ impact on system voltage stability. The theory of the normal forms of diffeomorphism is used to analyze the power flow equations, and then nonlinear active participation factor is obtained and is used to determine the influence of generators on voltage stability. By using this method, the nonlinearity of power systems can be taken into consideration while the computational efficiency is maintained. Therefore, the impact of generators can be measured with more accuracy even for the cases in which the system is characterized with strong nonlinearity. In order to show the validity of the proposed method, the IEEE 14-bus test system and the New England 39-bus power system are used as case studies. The steady-state voltage stability index verifies the proposed method. The results show that nonlinear active participation factor can describe the characteristics even when power systems are operating at a highly stressed condition.

Power systems in many countries are stressed towards their stability limit. If these stable systems experience any unexpected contingencies, or disturbances, there is a significant risk of instability, which may lead to wide-spread blackout. Existing methods to minimize the risk of stability and excessive frequency decline; need to be redeveloped to address these new challenges. This research project will develop a new emergency control methodology, which can more effectively prevent power system frequency and voltage instability under emergency conditions. Frequency and voltage instability are two major concerns in power system operation. The primary aim of this project is to develop new optimal load shedding techniques, which are able to better address various voltage and frequency instability issues for power systems emergency control purpose. In this thesis, new approach of load shedding for frequency and voltage stability are presented. For the load shedding to prevent frequency collapse, System Frequency Respond – Under Frequency Load Shedding (SFR-UFLS) from the previous approach has been redeveloped to compute an optimal load shedding scheme. The limitation of previous optimal load shed method is that they only shed load following one particular contingency event. As an improvement of this method, we developed a technique that protects against a range of contingencies. For the load shedding to prevent voltage collapse, The proposed method is then tested on the 39-bus New England test system. Generators are of different importance to the system in terms of voltage stability. It is essential to investigate generators’ impact on system voltage stability. The theory of the normal forms of diffeomorphism is used to analyze the power flow equations, and then nonlinear active participation factor is obtained and is used to determine the influence of generators on voltage stability. By using this method, the nonlinearity of power systems can be taken into consideration while the computational efficiency is maintained. Therefore, the impact of generators can be measured with more accuracy even for the cases in which the system is characterized with strong nonlinearity. In order to show the validity of the proposed method, the IEEE 14-bus test system and the New England 39-bus power system are used as case studies. The steady-state voltage stability index verifies the proposed method. The results show that nonlinear active participation factor can describe the characteristics even when power systems are operating at a highly stressed condition.

Power systems in many countries are stressed towards their stability limit. If these stable systems experience any unexpected contingencies, or disturbances, there is a significant risk of instability, which may lead to wide-spread blackout. Existing methods to minimize the risk of stability and excessive frequency decline; need to be redeveloped to address these new challenges. This research project will develop a new emergency control methodology, which can more effectively prevent power system frequency and voltage instability under emergency conditions. Frequency and voltage instability are two major concerns in power system operation. The primary aim of this project is to develop new optimal load shedding techniques, which are able to better address various voltage and frequency instability issues for power systems emergency control purpose. In this thesis, new approach of load shedding for frequency and voltage stability are presented. For the load shedding to prevent frequency collapse, System Frequency Respond – Under Frequency Load Shedding (SFR-UFLS) from the previous approach has been redeveloped to compute an optimal load shedding scheme. The limitation of previous optimal load shed method is that they only shed load following one particular contingency event. As an improvement of this method, we developed a technique that protects against a range of contingencies. For the load shedding to prevent voltage collapse, The proposed method is then tested on the 39-bus New England test system. Generators are of different importance to the system in terms of voltage stability. It is essential to investigate generators’ impact on system voltage stability. The theory of the normal forms of diffeomorphism is used to analyze the power flow equations, and then nonlinear active participation factor is obtained and is used to determine the influence of generators on voltage stability. By using this method, the nonlinearity of power systems can be taken into consideration while the computational efficiency is maintained. Therefore, the impact of generators can be measured with more accuracy even for the cases in which the system is characterized with strong nonlinearity. In order to show the validity of the proposed method, the IEEE 14-bus test system and the New England 39-bus power system are used as case studies. The steady-state voltage stability index verifies the proposed method. The results show that nonlinear active participation factor can describe the characteristics even when power systems are operating at a highly stressed condition.

Thesis (M.H.A.)--University of Michigan, 1957.