José Alexandre Gonçalves

Abstract

Abstract

Abstract

Abstract
In this paper it is described a new Servo Motor Optimised for Educational Robotic Applications (SMORA), which comprises several sensors and higher level addressing/communication capabilities. Even though servos have excellent qualities when used in the field of robotics, several properties can still be enhanced. These devices are known to have nonlinear properties and dynamic factors such as dead-zones, backlash and friction that might hinder their precision and accuracy. They usually do not provide position, velocity or current feedback to the device controlling them. Factors such as change in the load attached to the system increase the complexity of the analysis even further. Controlling these servos using a PWM signal might also become complicated if the number of servo motors to control increases. The new proposed servo consists of custom hardware and firmware that includes a microcontroller and a series of sensors, allowing for the motor current, temperature and voltage to be measured in real-time as well as precise position feedback thanks to the integrated hall-effect magnetic position encoder. It also incorporates an accelerometer and a gyroscope to measure the servo body relative position and rotation. © 2018 IEEE.

Abstract
This paper addresses the problem of finding the best Brain Emotional Learning (BEL) controller parameters in order to improve the response of a single degree-of-freedom (SDOF) structural system under an earthquake excitation. The control paradigm considered is based on a semi-active system to control the dynamics of a lumped mass-damper-spring model, being carried out by changing the damping force of a magneto-rheological (MR) damper. A typical BEL based controller requires the definition of several parameters which can be proved 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 (PSO) method was 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, simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually. © 2018 IEEE.