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
The use of smart phones for teaching and learning purposes is an increasingly important issue. MIT App-Inventor 2 is presented here as a tool to develop applications for Android mobile systems, with the global aim to improve the connectivity between teachers and students. Two recently developed applications using this technology: Teach2Student and Student2Teach are reported. Teach2Student has two main functions: i) to allow teachers to post their students courses main class's issues; ii) To provide students with specific quizzes. Student2Teach the corresponding students application, is used to visualize the posts and quizzes submitted by teachers. Preliminary results concerning the use of both applications within an industrial automation and control course are presented.

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

Abstract
In this paper we propose the gravitational search algorithm to design PID control structures. The controller design is performed considering the objectives of set-point tracking and disturbance rejection, minimizing the integral of the absolute error criterion. A two-degrees-of-freedom control configuration with a feedforward pre-filter inserted outside the PID feedback loop is used to improve system performance for both design criteria. The pre-filter used is a Posicast controller designed simultaneously with a PID controller. Simulation results are presented which show the proposed technique merit.