Abstract
The magnitude optimum (MO) method provides a relatively fast and non-oscillatory closed-loop tracking response for a large class of process models frequently encountered in the process and chemical industries. However, the deficiency of the method is poor disturbance rejection performance of some processes. in this paper, disturbance rejection performance of the PID controller is improved by applying the "disturbance rejection magnitude optimum" (DRMO) optimisation method, while the tracking performance has been improved by a set-point weighting and set-point filtering PID controller structure. The DRMO tuning method requires numerical optimisation for the calculation of PID controller parameters. The method was applied to two different 2-degrees-of-freedom PID controllers and has been tested on several different representatives of process models and one laboratory set-up. A comparison with some other tuning methods has shown that the proposed tuning method, with a set-point filtering PID controller, is quite efficient in improving disturbance rejection performance, while retaining tracking performance comparable with the original MO method.

Abstract
Technologies employed in greenhouse production systems have been developed considerably during the recent decades. These improvements have taken place in many different research areas, such as the development of new covering materials and actuating equipments, modeling of the plant physiological processes and greenhouse climate, new cultural techniques, among many other topics. Although, due to economic and environmental increasing requirements there is still a need to improve the tools used for greenhouse management. At the present this work is being addressed with the emphasis on the economic optimization of the production process. This approach implies to know the influence of climate factors on production, as well the establishment of a good interface between engineers, physiologists and biologists. This paper presents the methods that are being implemented and tested with the aim of improving the climate management of a greenhouse located in the UTAD-University campus.

Abstract
The paper addresses the problem of decreasing the overshoot for underdamped second-order systems. A new technique to control the overshoot is proposed, which is based on Posicast control and proportional integral and derivative (PID) control, which performs switching between two controllers. The aim is to use open-loop feedforward control to increase tracking performance and PID control to deal with disturbance rejection. It has been shown that the proposed control scheme can have some advantages over the classical approaches without switching capabilities.

Abstract
Derivative (D) terms in PID controllers are infrequently used in practice due to precise tuning requirements and noisy controller output. The first problem can be alleviated by using several modern tuning methods, while substantial decrease of controller activity can be achieved by filtering controller output. However, introducing additional filter at controller output can destabilise control loop if the PID controller parameters are not properly re-tuned. It has been shown that both problems can be solved simultaneously by using the magnitude optimum (MO) and disturbance rejection magnitude optimum (DRMO) tuning method. Moreover, it is shown that the PID controller performance can be higher than of the PI controller even at lower controller activity. The best controller performance vs. activity ratio is achieved with the second or the third order filter at controller output. © 2005 IEEE.

Abstract
This paper presents a tool for automating the design of road tunnels lighting systems. The tunnel lighting system must guarantee some minimal luminance values in order to ensure a easy driving and visual perception. The lights distribution, in different tunnel zones, is obtained in the proposed technique by using a genetic algorithm. The developed software framework automatically selects the best light type and its localization, according to a specified design objective, along the tunnel independently of the light manufacturer. © 2009 IEEE.

Abstract
This paper proposes a novel method for controlling the convergence rate of a particle swarm optimization algorithm using fractional calculus (FC) concepts. The optimization is tested for several well-known functions and the relationship between the fractional order velocity and the convergence of the algorithm is observed. The FC demonstrates a potential for interpreting evolution of the algorithm and to control its convergence.