An automatic tuner with short experiment and probabilistic plant parameterization

A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function. The method is demonstrated through industrially relevant examples. Robustness is enforced through probabilistic constraints on the H∞ norms of the sensitivity function, while minimizing load disturbance integral error (IE) to ensure performance. To demonstrate the strength of the proposed method, identification for the mentioned examples is carried out under a high level of measurement noise

Granular synthesis for display of time-varying probability densities

We present a method for displaying time-varying probabilistic information to users using an asynchronous granular synthesis technique. We extend the basic synthesis technique to include distribution over waveform source, spatial position, pitch and time inside waveforms. To enhance the synthesis in interactive contexts, we "quicken" the display by integrating predictions of user behaviour into the sonification. This includes summing the derivatives of the distribution during exploration of static densities, and using Monte-Carlo sampling to predict future user states in nonlinear dynamic systems. These techniques can be used to improve user performance in continuous control systems and in the interactive exploration of high dimensional spaces. This technique provides feedback from users potential goals, and their progress toward achieving them; modulating the feedback with quickening can help shape the users actions toward achieving these goals. We have applied these techniques to a simple nonlinear control problem as well as to the sonification of on-line probabilistic gesture recognition. We are applying these displays to mobile, gestural interfaces, where visual display is often impractical. The granular synthesis approach is theoretically elegant and easily applied in contexts where dynamic probabilistic displays are required

A hybrid approach to robustness analyses of flight control laws in re-entry applications

The present paper aims at improving the efficiency of the robustness analyses of flight control laws with respect to conventional techniques, especially when applied to vehicles following time-varying reference trajectories, such as in an atmospheric re-entry. A nonlinear robustness criterion is proposed, stemming from the practical stability framework, which allows dealing effectively with such cases. A novel approach is presented, which exploits the convexity of linear time varying systems, coupled to an approximate description of the original nonlinear system by a certain number of its time-varying linearizations. The suitability of the approximating systems is evaluated in a probabilistic fashion making use of the unscented transformation technique. The effectiveness and potentials of the method are ascertained by application to the robustness analysis of the longitudinal flight control laws of the Italian Aerospace Research Center (CIRA) experimental vehicle USV