ne of the main limiting factors in improving glucose control for T1DM subjects is the lack of a precise description of meal and insulin intake effects on blood glucose. Knowing magnitude and duration of such effects would be useful not only for patients and physicians but also for the development of a controller targeting glycemia regulation. Therefore, in this paper we focus on estimating low-com

The paper describes the design and verification of a self-adaptive system, composed of multiple smart cameras connected to a monitoring station, that determines the allocation of network bandwidth to the cameras. The design of such a system poses significant challenges, since multiple control strategies are active in the system simultaneously. In fact, the cameras adjust the quality of their strea

This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input- output sequences. The algorithms developed are autoregressive methods, methods of subspace-based model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output mode

Homogeneous Charge Compression Ignition (HCCI) holds promise for reduced emissions and increased efficiency compared to conventional internal combustion engines. As HCCI lacks direct actuation over the combustion phasing, much work has been devoted to designing controllers capable of set-point tracking and disturbance rejection. This paper presents results on model predictive control (MPC) of the

This paper shows an application of continuous-time system identification methods to Type 1 diabetes. First, a general MISO transfer function structure with individual nominator and denominator polynomials for each input is assumed and a parameter estimation procedure via an iterative prediction error method presented. Then, the proposed identification method is evaluated on a simple simulation exa

A drilling task requires a mechanism with five degrees of freedom, in order to achieve the correct position and orientation of the drilling tool. When performed with a standard 6-axes industrial robot, this task leaves an extra degree of freedom that can be exploited in order to achieve any additional criterion. Unfortunately, typical industrial robotic control architectures do not allow the user

Modern industrial robots are fast and have very good repetitional accuracy, which have made them indispensable in many manufacturing applications. However, they are usually programmed to follow desired trajectories and only get feedback from position sensors. This works fine as long as the environment is very well structured, but does not give good robustness to objects not being positioned or gri

This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input-output sequences. The algorithms developed are autoregressive methods, methods of subspace-based model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model

The focus of this article is to undertake a comparative analysis of multi-step-ahead linear multivariate predictors. The approach considered for the estimation will be based on geometrically reliable linear algebra tools, resorting to subspace identification methods. A crucial issue is quantification of both bias error and variance affecting the estimate of the prediction for increasing values of

The well-known and commonly accepted finite dimensional model qualitatively describing surge instabilities in centrifugal (and axial) compressors is considered. The problem of global output feedback stabilization for it is solved. The solution relies on two new criteria for global stability proposed for a class of nonlinear systems exploiting quadratic constraints for infinite sector nonlinearitie

This paper presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite non-uniformly sampled input-output sequences. The algorithms developed are methods of model identification and stochastic realization adapted to the continuous-time model context using non-uniformly sampled input-output data. The resulting model can be decompose

Using methods of system identification and prediction, we investigate near-future prediction of individual specific T1DM blood glucose dynamics with the purpose of a decision-making tool development in diabetes treatment. Two strategies were approached: Firstly, Kalman estimators based on identified state-space models were designed; Secondly, direct identification of ARX- and ARMAX-based predictor

Stability problems associated with robot control with obstacle avoidance are analyzed.Obstacle avoidance algorithms based on potential function are revised to accomplish stable re-design. A stability proof using Lyapunov theory and passivity theory is provided for the re-designed obstacle avoidance algorithm. The paper presents a modification of potential functions for obstacle avoidance aiming to

Control of robotic systems subject to friction phenomena is an important issue since growing demands on accuracy require elimination of friction disturbances. If several modelse.g., friction model, rigid-body dynamicsare required to describe the behavior with high precision, each model requires the knowledge of numerous parameters (perhaps time-varying) as well as an increased number of signals an

To facilitate model-based control of combustion timing and work output in Homogeneous Charge Compression Ignition (HCCI) engines, a cycle-resolved model of HCCI incorporating cylinder wall temperature dynamics is presented. Heat transfer between the in-cylinder charge and the cylinder walls is important for explaining HCCI cycle-to-cycle behaviour but is rarely included in control-oriented models.