Abstract
This paper aims to evaluate the performance of a semi-active controlled suspension system using a magneto-rheological (MR) damper to provide better ride comfort and safety to vehicle passengers than an uncontrolled or passive suspension system. Passive systems represent a conventional solution for vibration control of suspension systems. Although this system is a proven, reliable and economic technology, their parameters cannot be modified according to the road conditions. On the other hand, active systems allow a continuous control of the suspension motion, but require a complex and energy demanding actuator. The proposed suspension system has the adaptability of active systems with lower energy consumption, which constitute an economic and efficient option for vibration control in vehicle suspensions. The analysis was carried out with a set of numerical simulations in Matlab/Simulink using a 1/4 vehicle suspension model with two degrees of freedom for a passive system and two semiactive control modes based on fuzzy and optimal controllers.

Abstract
Typical vehicle suspension systems are based on passive energy dissipation devices. This type of systems have proven to be a reliable and economic approach, however they are not capable to modify its behavior in accordance with the road conditions. On the other hand, active systems allow a continuous control of the suspension response although requiring sensors, actuators and controllers which represents a more complex and expensive system, usually demanding high power requirements. A middle-term vibration control approach is to use the so-called semi-active systems with the adaptability of active systems and lower energy consumption. This paper aims to evaluate the comfort ridding of a full suspension bicycle equipped with semi-active open loop controlled suspension system using a magneto-rheological (MR) damper. The assessment was carried out based on the analysis of real data, extracted from the instrumented bicycle prototype. The experimental tests were made in a smooth indoor pavement and a cobblestone road. Finally, the results obtained with the proposed semi-active suspension control system are presented and discussed.

Abstract
In this paper it is described the prototyping of an instrumented chair that allows to fully-automate the "Timed Up and Go", the "30-Second Chair Stand" and the "Hand-Force "tests assessment. The presented functional chair prototype is a low cost approach that uses inexpensive sensors and the Arduino platform as the data acquisition board, with its software developed in LabVIEW. The "Timed up and go test" consists in measuring the time spent in the task execution of standing up from a chair, walk three meters with a maximum speed without running, turn a cone and going back to the initial position. The "30-Second Chair Stand" test consists in counting the number of completed chair stands in 30 seconds. It are agility, strength and endurance tests easy to setup and execute although they lack of repeatability, whenever the measures are taken manually, due to the rough errors that are introduced. The "Hand-Force" test consists in measuring the hand strength, the relevant data are the peak and average values of several tests. The referred data is important in order to evaluate hand rehabilitation treatment results.

Abstract
This work addresses the problem of finding the best controller parameters in order to improve the response of a single degree-of-freedom structural system under earthquake excitation. The control paradigm considered is based on brain emotional learning (BEL) and the actuation over the building dynamics is carried out by changing the stiffness of a magneto-rheological damper. A typical BEL-based controller requires the definition of several parameters which can prove difficult and non-intuitive to obtain. For this reason, an evolutionary-based search technique has been added to the current problem framework in order to automate the controller design. In particular, the particle swarm optimization method is chosen as the evolutionary based technique to be integrated within the current control paradigm. The obtained results suggest that, indeed, it is possible to parametrize a BEL controller using an evolutionary-based algorithm. Moreover, a simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually.

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