Safety concerns have been keeping autonomous vehicles off the roads for decades, although the main drivers for introducing some autonomy are to increase safety, reduce congestion, and greenhouse gas emissions. Safety is a vast topic that includes the safety of the system alone, known as string stability, and the safety of the system on public roads. This thesis provides experimental validation of the string stability of the Assume-Guarantee approach. This approach suggests that each agent models the interactions with neighbors as bounded disturbances while simultaneously self-imposing symmetric magnitude bounds. Two main controllers were tested in an indoor lab set-up: decentralized platooning and decentralized cooperative adaptive cruise controllers. First, we tested three versions of the platooning controller whose objective is to maintain a constant spacing. They differ in the assumptions and guarantees. We observed a robust performance with relaxed bounds and some violations as the bounds become tighter and tighter. Second, we modified and transformed the platoon model into cooperative adaptive cruise control (CACC). Unlike the platoon controller, the cooperative adaptive cruise controller keeps the time gap constant. Two experiments were conducted at different velocities to evaluate the limitation of the controller. The results show a stable and smooth performance.

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Os sinais de eletromiografia de agulha podem ser modelados por um sistema linear invariante no tempo (SLIT). A pergunta é: Quantos coeficientes são necessários para tal? O presente mestrado estuda, para sinais de eletromiografia de agulha gravados sob as mesmas condições experimentais, como varia o número ótimo de coeficientes autorregressivos com o comprimento das épocas e com a força de contração muscular concomitantemente. O estudo foi realizado tendo como base sinais de 10%, 25%, 50% e 80% da máxima contração voluntária (MCV) e tendo épocas de 500ms, 250ms, 100ms, 50ms e 25ms de seis indivíduos normais. Desta forma, uma função densidade de probabilidade é sugerida para a ordem do modelo autorregressivo que melhor descreva o sinal de eletromiografia obtido a uma força de contração específica e que tenha uma duração de época definida. / Needle electromyography signals (EMG) can be modeled by a linear time invariant system (LTI). The posed question is How many coefficients are needed for an adequate modeling? This Masters dissertation studies how the optimal number of autoregressive coefficients changes concomitantly with the epoch length and the muscle contraction force for needle electromyography signals recorded under the same experimental conditions. The study was conducted on signals from six normal individuals at 10%, 25%, 50% and 80% of the maximum voluntary contraction and epoch lengths of 500ms, 250ms, 100ms, 50ms and 25ms. Thus, a probability density function is suggested for the autoregressive model order that best describes the electromyographic signal obtained at a specific \"contraction force\" and has a defined \"epoch length\".

Os sinais de eletromiografia de agulha podem ser modelados por um sistema linear invariante no tempo (SLIT). A pergunta é: Quantos coeficientes são necessários para tal? O presente mestrado estuda, para sinais de eletromiografia de agulha gravados sob as mesmas condições experimentais, como varia o número ótimo de coeficientes autorregressivos com o comprimento das épocas e com a força de contração muscular concomitantemente. O estudo foi realizado tendo como base sinais de 10%, 25%, 50% e 80% da máxima contração voluntária (MCV) e tendo épocas de 500ms, 250ms, 100ms, 50ms e 25ms de seis indivíduos normais. Desta forma, uma função densidade de probabilidade é sugerida para a ordem do modelo autorregressivo que melhor descreva o sinal de eletromiografia obtido a uma força de contração específica e que tenha uma duração de época definida. / Needle electromyography signals (EMG) can be modeled by a linear time invariant system (LTI). The posed question is How many coefficients are needed for an adequate modeling? This Masters dissertation studies how the optimal number of autoregressive coefficients changes concomitantly with the epoch length and the muscle contraction force for needle electromyography signals recorded under the same experimental conditions. The study was conducted on signals from six normal individuals at 10%, 25%, 50% and 80% of the maximum voluntary contraction and epoch lengths of 500ms, 250ms, 100ms, 50ms and 25ms. Thus, a probability density function is suggested for the autoregressive model order that best describes the electromyographic signal obtained at a specific \"contraction force\" and has a defined \"epoch length\".

While moment matching can effectively reduce the dimension of a linear, time-invariant system, it can simultaneously fail to improve the stable time-step for the forward Euler scheme. In the context of a semi-discrete heat equation with spatially smooth forcing, the high frequency modes are virtually insignificant. Eliminating such modes dramatically improves the stable time-step without sacrificing output accuracy. This is accomplished by modal filtration, whose computational cost is relatively palatable when applied following an initial reduction stage by moment matching. A bound on the norm of the difference between the transfer functions of the moment-matched system and its modally-filtered counterpart yields an intelligent choice for the mode of truncation. The dual-stage algorithm disappoints in the context of highly nonnormal semi-discrete convection-diffusion equations. There, moment matching can be ineffective in dimension reduction, precluding a cost-effective modal filtering step.