This study addresses the control of linear systems subject to both sensor and actuator saturations and additive L2-bounded disturbances. Supposing that only the output of the linear plant is measurable, the synthesis of stabilising output feedback dynamic controllers, allowing to ensure the internal closed-loop stability and the finite L2-gain stabilisation, is considered. In this case, it is shown that the closed-loop system presents a nested saturation term. Therefore, based on the use of some modified sector conditions and appropriate variable changes, synthesis conditions in a quasi-linear matrix inequality (LMI) form are stated in both regional (local) as well as global stability contexts. Different LMI-based optimisation problems for computing a controller in order to maximise the disturbance tolerance, the disturbance rejection or the region of stability of the closed-loop system are proposed.

In this article, a new approach to the problem of indoor navigation based on ultrasonic sensors is presented, where artificial neural networks (ANN) are used to estimate the position and orientation of a mobile robot. This approach proposes the use of three Radial Basis Function (RBF) Networks, where environment maps from an ultrasonic sensor and maps synthetically generated are used to estimate the robot localization. The mobile robot is mainly characterized by its real time operation based on the Matlab/Simulink environment, where the whole necessary tasks for an autonomous navigation are done in a hierarchical and easy reprogramming way. Finally, practical results of real time navigation related to robot localization in a known indoor environment are shown.

Data-based control design methods most often consist of iterative adjustment of the controller's parameters towards the parameter values which minimize an H2 performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization - no process model is used. The convergence to the global minimum of the performance criterion depends on the initial controller parameters and on the step size of each iteration. This paper discusses these issues and provides a method for choosing the step size to ensure convergence to the global minimum utilizing the lowest possible number of iterations.

PURPOSE: The aim of the present crossover study was to evaluate professional toothbrushing as a method for diagnosing gingivitis in children. MATERIALS AND METHODS: Thirty-four preschool children who had gingival bleeding > or = 10% and without proximal restorations/carious lesions were included in the study. Examinations comprised two gingival indices recorded at a 15-min interval, in the following sequences: the Ainamo and Bay gingival bleeding index (GBI1) followed by the brushing index (BI2) and vice versa (BI1-GBI2). Half of the children started the study in the first sequence and the other half in the second. After a 3- to 4-day washout period, the indices were again recorded with individuals changing the sequences. Data analysis considered GBI as the gold standard, and sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) were also calculated. RESULTS: The overall mean value of gingival bleeding at the first examination was 18.85 +/- 9.24%. Validation of toothbrushing performed after GBI (BI2) resulted in values for SE, SP, PPV and NPV of 0.59 (95% CI, 0.55 to 0.63), 0.92 (95% CI, 0.91 to 0.93), 0.64 (95% CI, 0.60 to 0.69) and 0.90 (95% CI, 0.89 to 0.92), respectively. BI performed before GBI (BI1) resulted in similar SE, SP, PPV and NPV. Percentage agreement of GBI1-BI2 and BI1-GBI2 was 83.5% and 85.9%, respectively. CONCLUSIONS: Professional toothbrushing can be suggested as a method for the diagnosis of gingival inflammatory status in children, especially as an indicator of gingival health.