Abstract
In this work, the feasibility of applying a Sliding Mode Predictive Controller (SMPC) to improve greenhouse inside air temperature control is addressed in terms of energy consumption, disturbance handling and set point tracking accuracy. Major research issues addressed concern the SMPC robustness study in greenhouse control, as well as to evaluate if the levels of performance and energy consumptions are acceptable when compared with the traditional generalized predictive controller. Besides the external disturbances related to weather conditions throughout the considered period, such as solar radiation and temperature variations, internal effects of irrigation system and external air flow entering the greenhouse must be taken into account. Simulations based on real data, carried out for a period of 4months, suggest that the strategy herein described not only appropriately rejects these disturbances, but also keeps the manipulated variables (heating and cooling) within feasible practical limits, with low levels of energy consumption, motivating its refinement for real application. SMPC results are presented and compared with the ones obtained with the generalized predictive controller. Both controllers are subject to actuator constraints and employ the Quadratic Programming for optimization. Copyright (c) 2015 John Wiley & Sons, Ltd.

Abstract
A teaching experiment is proposed in which an artificial intelligence technique is blended with classical control techniques to design PID controllers. The artificial intelligence technique deployed is currently considered one of the most popular and successfully nature and biological inspired metaheuristics: the particle swarm optimization algorithm. The teaching experiment is proposed for an introductory undergraduate Biomedical Engineering feedback control systems course. The mean arterial pressure control, quite relevant in practical application terms, is revisited. Moreover, another biomedical control problem is proposed for teaching/learning purposes: the minimum temperature control for intracranial tumor treatment. Simulation results concerning both classic and artificial intelligence based techniques for PID controller design are presented.

Abstract
Nature and biological inspired search and optimization methods are simple and powerful tools that can be used to design classical industrial controllers. In this paper a particle swarm optimization (PSO) algorithm based technique is deployed to design proportional integrative and derivative controllers to fulfill minimum robustness constraints. PID robustness design using maximum sensitivity and complementary sensitivity values is re-addressed and formulated within a constrained PSO. Results are presented and analyzed regarding the control objective of load disturbance rejection and compared with other techniques.

Abstract
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 (R) code are provided. The included code illustrates both the algebraic and the graphical nature of the coefficient diagram design method.

Abstract
The refinement of an existing internet multimedia tool to support teaching/learning activities concerning GRAFCET is reported here. The tool called c-GRAFCET resulted in an updated and improved version called e-GRAFCET+. The overall objective of this internet-based tool is to provide fundamental aspects regarding GRAFCET presented with animated examples, promoting students self-learning. While originally this tool was developed to be used by Portuguese speakers, now it is also available in English. The tool upgrading resulted in the elaboration of a new internet portal, incorporating new features, concerning the site management and question quizzes updating. A very important feature of e-GRACFCET+, is to be prepared to work on most devices regardless of operating systems and screen sins, such as smart-phones and tablets.

Abstract
The coefficient diagram method (CDM) is one of the easiest methods for model based control system design. Its core is based on an algebraic method but it also encompasses a graphical analysis diagram that helps the user to evaluate the three main closed-loop system requirements: dynamic behaviour, robustness and stability. This later characteristic is analysed by a set of stability conditions derived from the previous work of Lipatov and Sokolov on sufficient conditions for stability. However, in CDM, only a fraction of the total conditions are considered. This work will show that this fact increases the inconclusive area within the stability space. Moreover an extended set of CDM stability conditions, in conjunction with its graphical interpretation, will be presented.