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In this dissertation, we seek to improve the dynamic modeling and control of autonomous underwater vehicles (AUVs). We address nonlinear hydrodynamic modeling, simplifying modeling assumptions, and robust control for AUVs. In the literature, various hydrodynamic models exist with varying model complexity and with no universally accepted model. We compare various hydrodynamic models traditionally employed to predict the motion of AUVs by estimating model coefficients using least-squares and adaptive identifier techniques. Additionally, we derive several dynamic models for an AUV employing varying sets of simplifying assumptions. We experimentally assess the efficacy of invoking typical assumptions to simplify the equations of motion. For robust control design, we develop a procedure for designing robust attitude controllers based on loop-shaping ideas. We specifically address the challenge of adjusting the desired actuator bandwidth in a loop-shaping design framework. Finally, we present a novel receding horizon H-infinity control algorithm to improve the control of autonomous vehicle systems working in high-disturbance environments, employing a Markov jump linear system framework to model the stochastic and non-stationary disturbances experienced by the vehicle. Our main results include a new Bounded Real Lemma for stability analysis and an output feedback H-infinity control synthesis algorithm. This work uses numerical simulations and extensive field trials of autonomous underwater vehicles to identify and verify dynamic models and to validate control algorithms developed herein. / Ph. D. / In this dissertation, we seek to improve the dynamic modeling and control of autonomous underwater vehicles (AUVs). We compare different models employed to predict the motion of AUVs, and we derive several dynamic models for an AUV employing varying sets of simplifying assumptions. We experimentally assess the efficacy of invoking typical assumptions to simplify the equations of motion. For robust control design, we develop a procedure for designing robust controllers that do not produce excessive fin movements. Finally, we present a novel robust control algorithm to improve the control of autonomous vehicle systems working in high-disturbance environments. This work uses numerical simulations and extensive field trials of autonomous underwater vehicles to identify and verify dynamic models and to validate control algorithms developed herein.

The in-flight catering industry is a major contributor to food wastage. This wastage is a direct result of the deliberate overproduction of in-flight meals to protect against meal shortages and dissatisfied passengers. With the global strive towards sustainability and the resulting impact of wastage on a company's corporate image, in-flight catering companies need a solution that strives to achieve zero waste and a 100% passenger satisfaction level. This dissertation evaluates the value of combining product substitution and demand uncertainty within an inventory decision-making model as a potential solution opportunity for the wastage dilemma faced by the in-flight catering industry. The decision-making model's purpose is to assist in-flight caterers to make improved decisions regarding the quantity of each meal type to produce for the specific flight under consideration. The model developed is defined as a stochastic multi-objective mixed-integer programming model with fixed recourse and two-way, stock-out based, partial consumer-driven (static) product substitution. The model relies on the output of a forecasting model, that consists of a time-inhomogeneous Markov Chain and a multiple regression model, to forecast the probability distribution of a flight's aggregate meal demand. Due to the lack of available data from public sources, synthetic data is generated to evaluate the model developed. The model is compared against three alternative models that lack either demand uncertainty, product substitution or both to validate the value of including these elements in the decision-making model. The comparison results indicate that the inclusion of the passenger load uncertainty improves the model's average reliability to achieve a 92% minimum passenger satisfaction level with at least 9.2%. Furthermore, it is shown that the stochastic passenger load model produces an average of 2.2 fewer surplus meals per flight instance at the expense of a 3.3% lower reliability when including the substitution behaviour of passengers. This substitution model's superior waste minimisation is attributed to the model's inherent risk-pooling capabilities, and further analysis shows that the value of product substitution increases when the model becomes more constrained. It is, therefore, concluded that the value of product substitution depends on the in-flight caterer's bias towards maximising either reliability or performance. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2021. / Council for Scientific and Industrial Research (CSIR) / Industrial and Systems Engineering / MEng / Unrestricted

<p>Today's highly integrated supply chains are exposed to various types of risks which disrupt the normal flow of goods or services within a supply chain network. Since most of these individual risks are interconnected, a mitigation strategy to tackle one risk may result in the exacerbation of another.</p> <p>Risk dependencies have been modelled using two approaches in the financial insurance literature : (i) random variables, and (ii) copulas. In this dissertation these studies are reviewed and extended. Also, applications for these models for different supply chain network configurations are presented. Then, a Poisson process model for risk propagation is proposed. Unlike the existing models, the transition rate of the proposed model not only expresses the time dependency, but also captures other possible dependencies in the network. Finally, the thesis is summarized and general directions and suggestions for future research on risk dependency and propagation modelling are provided.</p> / Master of Science (MSc)

Markov jump linear systems find application in many areas including economics, fault-tolerant control, and networked control. Despite significant attention paid to Markov jump linear systems in the literature, few authors have investigated Markov jump linear systems with time-inhomogeneous Markov chains (Markov chains with time-varying transition probabilities), and even fewer authors have considered time-inhomogeneous Markov chains with a priori unknown transition probabilities. This dissertation provides a formal stability and disturbance attenuation analysis for a Markov jump linear system where the underlying Markov chain is characterized by an a priori unknown sequence of transition probability matrices that assumes one of finitely-many values at each time instant. Necessary and sufficient conditions for uniform stochastic stability and uniform stochastic disturbance attenuation are reported. In both cases, conditions are expressed as a set of finite-dimensional linear matrix inequalities (LMIs) that can be solved efficiently. These finite-dimensional LMI analysis results lead to nonconservative LMI formulations for optimal controller synthesis with respect to disturbance attenuation. As a special case, the analysis also applies to a Markov jump linear system with known transition probabilities that vary in a finite set. / Ph. D.