Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Yes / Probability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense. / De Montfort University - DMU HEIF’18 project, Natural Science Foundation of Shanxi Province [grant number 201701D221112], National Natural Science Foundation of China [grant numbers 61503271 and 61603136]

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

This thesis explores lobbying as an important political economy dimension of policymaking. It exploits theoretical, empirical, and numerical approaches and methods to investigate the possibilities of engaging in costly lobbying and how lobbying by special interests affects the setting of minimum wage and small business tax rates. The theoretical modeling relies on the common agency framework - a situation with multiple principals who are simultaneously and non-cooperatively interacting with a single agent - of public policy lobbying and a simpler principal agent model. Empirical analysis employs panel data regression methods in the context of Canadian provinces to identify causal relationship. Both minimum wage and small business taxation invite a considerable amount of activity from various special interest groups in Canada, which engage in lobbying for a policy stance more favorable to their members. After providing a brief overview of lobbying issues and literature in the first chapter, in the second one I show that initial lobbying cost can be a clear entry barrier, that lobbying competition can have properties of a high-stakes game and that lobbying can take place simply to preserve the status quo and not lose ground. In the pure rivalry sense, to not allow the opponent to gain ground in the policy arena. In the third chapter, I formulate a model of minimum wage determination based on the common agency lobbying framework to evaluate how the competition for political influence between unionized workers and firm owners affects the minimum wage determination. A binding minimum wage is a function of the policymaker's political ideology, the labor demand elasticity and the skill composition of union members. Specifically, when the elasticity of labor demand is large, the benefit of lobbying against (for) an increase in the minimum wage is greater since a potential minimum wage increase has a larger negative (positive) effect on firms' (unionized workers') income. Lobbying is successful in inducing the policymaker to set the minimum wage in accordance with her political preference; a more business (labor) friendly policymaker reduces (increases) the minimum wage. However, lobbying can also induce the policymaker to go against its ideological preference. Empirical analysis on a panel data for ten Canadian provinces over the 1965-2013 period gives considerable support for theoretical predictions. Preferred panel data regression specifications, controlling for unobserved province and year effects, and various province specific, time varying factors, indicate that real minimum wage decreases in skill-adjusted union density and a measure of political ideology, and increases with technological progress. Greater labor demand elasticity reinforces the influence of political ideology in the presence of lobbying. In the fourth chapter, I focus on the issue of small business tax determination and the effect of lowering its rate on income inequality. In Canada, where the small business income tax rate is considerably lower than the top individual rate, higher income individuals are able to reduce their personal taxes by retaining and shifting income via privately owned small businesses. Therefore, because the small business owners benefit from an increasing difference between the small business and top individual tax rates, I show using a principal-agent model that by lobbying as a special interest group they can always `buy' a lower corporate tax rate from the government. However, a lower business income tax, relative to a given personal income tax rate, is not income inequality neutral and unambiguously increases the income share of the highest earning individuals in the economy, specifically those who own small corporations. / Thesis / Doctor of Philosophy (PhD)

In motorsport, simulating road vehicles driving at the limit of handling is a valuable tool to study and optimize their overall performance during the design and set-up phases. Along with Quasi-Steady-State optimization, optimal control (OC) is the most utilized technique to simulate the control and states of a vehicle during minimum-time maneuvers and has been used for offline lap-time optimization for more than twenty years now. Since the first applications of optimal control in this field, it has been clear that the solution of the minimum-time optimization does not represent a model of the human driver but instead substitutes him/her. However, the common points or divergences between the minimum-time strategy of human race drivers and the OC one are still unclear. Moreover, it seems that in the literature there is no agreement about what vehicle models must be used, and in general the choice of one model or the other is not clearly justified. Finally, thanks to the rise in popularity of autonomous driving and racing, optimal control has been used as path planner for automated vehicles: %nonetheless, the application of free-trajectory real-time nonlinear optimal control in Model Predictive Control (MPC) schemes, where the optimal controls are directly fed to the vehicle, is still an unexplored topic. nonetheless, the application of free-trajectory real-time nonlinear optimal control in Model Predictive Control (MPC) schemes, where the optimal controls are computed from a single optimization and directly fed to the vehicle, is a topic still open for exploration. The first aim of this thesis is to provide an objective comparison of several vehicle, tire, powertrain and road models to be used in minimum-time OC. In the first part of this work we thus detail several models of the vehicle and its subsystems. We then solve minimum-time OC problems on a series of test tracks adopting most of the model combinations and discuss the differences in the solutions. We then draw conclusions on the best model combinations to obtain realistic and reliable minimum-time maneuvers. The second part of the thesis aims to prove that the solutions of minimum-time OC problems are indeed different from the driving behavior of professional drivers, but they can be employed to coach the human driver and improve his/her racing performance. After modeling a high-performance vehicle manufactured by Ferrari, we again use optimal control to compute minimum-time maneuvers on two different tracks. A professional racer driving is then coached in following the OC strategy on the Ferrari driving simulator, and we objectively prove that the driver can outperform his previous lap times. In the third and last part of the thesis, we aim to prove that free-trajectory real-time optimal control is a valid alternative to hierarchical MPC frameworks based on high-level path planning and low-level path tracing. We first develop a novel kineto-dynamic vehicle model able to satisfy the trade-off between computational lightness and accuracy in representing the vehicle's pure and combined dynamics. Then, by solving a minimum-time OC in real-time, we are able to autonomously drive a real scaled vehicle around a track at the limits of tire adherence.