Series elastic actuators (SEAs) have become fundamental components in robots that physically interact with unstructured environments and humans. Force control of SEAs is indeed an active area of research. This paper proposes a theoretical foundation for acceleration feedback (AF) in SEA force control. Even if AF already appeared in early works on SEAs, its advantages have not been properly highlighted in the literature. In particular, this paper formally motivates improved performance robustness and transparency exactly as if using a softer and lighter actuator. Taking advantage of AF, we propose a generic control architecture characterized by impressive performance robustness in spite of even high environment uncertainties. A comparison with state-of-the-art force control solutions such as disturbance observers and adaptive controllers is reported using a comprehensive set of simulations and experiments. As a result, AF methods exhibit the higher performance robustness and accuracy. Beside this outcome, AF controllers are extremely easy to implement and the rise of low-cost miniaturized accelerometers based on micro electro-mechanical systems (MEMS) represents an additional motivations for their use.

We discuss the use of low-dimensional physical models of the voice source for speech coding and processing applications. A class of waveform-adaptive dynamic glottal models and param-eter tracking procedures are illustrated. The model and analysis procedures are assessed by addressing signal transformations on recorded speech, achievable by fitting the model to the data, and then acting on the physically-oriented parameters of the voice source. The class of models proposed provides in prin-ciple a tool for both the estimation of glottal source signals, and the encoding of the speech signal for transformation pur-poses. The application of this model to time stretching and to frequency control (pitch shifting) is also illustrated. The exper-iments show that copy synthesis is perceptually almost indistin-guishable form the target, and that time stretching and "pitch extrapolation" effects can be obtained by simple control strate-gies.

We discuss the use of low-dimensional physical models of the voice source for speech coding and processing applications. A class of waveform-adaptive dynamic glottal models and parameter tracking procedures are illustrated. The model and analysis procedures are assessed by addressing speech encoding and enhancement, achievable by using a state space version of the dynamical model in a Extended Kalman filtering framework. The proposed method is shown to provide better SNR improvement if compared to a standard AR Kalman filtering scheme.