LQG control policy design against agent identity privacy problem

This thesis project studies the agent identity privacy problem in the scalar linear quadratic Gaussian (LQG) control system. For the agent identity privacy problem in the LQG control, privacy models and privacy measures have to be established first. It depends on a trajectory of correlated data rather than a single observation. I propose here privacy models and the corresponding privacy measures by taking into account the two characteristics. The agent identity is a binary hypothesis: Agent A or Agent B. An eavesdropper is assumed to make a hypothesis testing on the agent identity based on the intercepted environment state sequence. The privacy risk is measured by the Kullback-Leibler divergence between the probability distributions of state sequences under two hypotheses. By taking into account both the accumulative control reward and privacy risk, an optimization problem of the policy of Agent B is formulated. The optimal deterministic privacy-preserving LQG policy of Agent B is a linear mapping. A sufficient condition is given to guarantee that the optimal deterministic privacy-preserving policy is time-invariant in the asymptotic regime. An independent Gaussian random variable cannot improve the performance of Agent B. The numerical experiments justify the theoretic results and illustrate the reward-privacy trade-off. Based on the privacy model and the LQG control model, I have formulated the mathematical problems for the agent identity privacy problem in LQG. The formulated problems address the two design objectives: to maximize the control reward and to minimize the privacy risk. I have conducted theoretic analysis on the LQG control policy in the agent identity privacy problem and the trade-off between the control reward and the privacy risk.Finally, the theoretic results are justified by numerical experiments. From the numerical results, I expected to have some interesting observations and insights, which are explained in the last chapter.

A new concept for a Sport-Coupè car developed with the Industrial Design Structure method (IDeS)

The aim of this study, conducted in collaboration with Lawrence Technological University in Detroit, is to create, through the method of the Industrial Design Structure (IDeS), a new concept for a sport-coupe car, based on a restyling of a retro model (Ford Mustang 1967). To date, vintage models of cars always arouse great interest both for the history behind them and for the classic and elegant style. Designing a model of a vehicle that can combine the charm of retro style with the innovation and comfort of modern cars would allow to meet the needs and desires of a large segment of the market that today is forced to choose between past and future. Thanks to a well-conceived concept car an automaker company is able to express its future policy, to make a statement of intent as, such a prototype, ticks all the boxes, from glamour and visual wow-factor to technical intrigue and design fascination. IDeS is an approach that makes use of many engineering tools to realize a study developed on several steps that must be meticulously organized and timed. With a deep analysis of the trends dominating the automotive industry it is possible to identify a series of product requirements using quality function deployment (QFD). The considerations from this first evaluation led to the definition of the technical specifications via benchmarking (BM) and top-flop analysis (TFA). Then, the structured methodology of stylistic design engineering (SDE) is applied through six phases: (1) stylistic trends analysis; (2) sketches; (3) 2D CAD drawings; (4) 3D CAD models; (5) virtual prototyping; (6) solid stylistic model. Finally, Developing the IDeS method up to the final stages of Prototypes and Testing you get a product as close as possible to the ideal vehicle conceptualized in the initial analysis.