Brain emotional learning based control of a SDOF structural system with a MR damper

This paper describes the application of a Brain Emotional Learning (BEL) controller to improve the response of a SDOF structural system under an earthquake excitation using a magnetorheological (MR) damper. The main goal is to study the performance of a BEL based semi-active control system to generate the control signal for a MR damper. The proposed approach consists of a two controllers: a primary controller based on a BEL algorithm that determines the desired damping force from the system response and a secondary controller that modifies the input current to the MR damper to generate a reference damping force. A parametric model of the damper is used to predict the damping force based on the piston motion and also the current input. A Simulink model of the structural system is developed to analyze the effectiveness of the semi-active controller. Finally, the numerical results are presented and discussed.

Controller system design using the coefficient diagram method

Coefficient diagram method is a controller design technique for linear time-invariant systems. This design procedure occurs into two different domains: an algebraic and a graphical. The former is closely paired to a conventional pole placement method and the latter consists on a diagram whose reading from the plotted curves leads to insights regarding closed-loop control system time response, stability and robustness. The controller structure has two degrees of freedom and the design process leads to both low overshoot closed-loop time response and good robustness performance regarding mismatches between the real system and the design model. This article presents an overview on this design method. In order to make more transparent the presented theoretical concepts, examples in Matlab®code are provided. The included code illustrates both the algebraic and the graphical nature of the coefficient diagram design method. © 2016, King Fahd University of Petroleum & Minerals.

Acoustic analysis of vocal dysphonia

Voice acoustic analysis is becoming more and more usefúl in diagnosis of voice disorders or laryngological pathologies. The facility to record a voice sigiial is an advantage over other invasive techniques. This paper presents the statistical analyzes ofa set of voice parameters like jitter, shimmer and HNR over a 4 groups of subjects vvith dysphonia, fünctional dysphonia, hyperfünctional dysphonia, and psychogenic dysphonia and a control group. No statistical signifícance differences over pathologic groups were found but clear tendencies can be seen between pathologic and control group. The tendencies indicates this parameters as a good features to be used in an intelligent diagnosis system, moreover the jitter and shimmer parameters measured over different tones and vowels.