Abstract
Soil water content has a direct influence on the cooling and growth mechanisms of the plants. Crop evapotranspiration is majoring influenced by solar radiation and the air temperature, humidity and movement. An efficient irrigation system must supply and maintain, at soil root zone of the plants, the adequate amount of water that best regulate the physiological mechanisms of the plant. For this purpose, an intelligent real-time greenhouse irrigation system was implemented which uses accurate sensors for measuring soil moisture, as well to determinate the crop evapotranspiration. To avoid loss of control, it was provided fault-detection capabilities to the soil moisture sensor and used a knowledge-based approach to estimate replacement values for the faulty sensors. A model-based controller was implemented to regulate the water content at the root zone of the plants. These tasks are very complex and difficult to meet, unless microcontroller and microprocessor systems are employed, such as in the integrated management irrigation system. The system comprises four modules: Sensor/Actuation, Acquisition and Data Validation, Data Correction, Model Based Controller, and Control Signals Generation. All the modules are linked and supervised by a higher-level supervision module to achieve an intelligent irrigation.

Abstract
The particle swarm optimisation algorithm is proposed as a new method to design a model-based predictive greenhouse air temperature controller subject to restrictions. Its performance is compared with the ones obtained by using genetic and sequential quadratic programming algorithms to solve the constrained optimisation air temperature control problem. Controller outputs are computed in order to optimise future behaviour of the greenhouse environment, regarding set-point tracking and minimisation of the control effort over a prediction horizon of I h with 1-min sampling period, for a greenhouse located in the north of Portugal. Since the controller must be able to predict the greenhouse environmental conditions over the specified time interval, it is necessary to use mathematical models that describe the greenhouse climate, as well as to predict the outside weather. These requirements are met by using auto regressive models with exogenous inputs and time series auto-regressive models to simulate the inside and outside climate conditions, respectively. These models have time variant parameters and so, recursive identification techniques are applied to estimate their values in real-time. The models employ data from the climate inside and outside the greenhouse, as well as from the control inputs. Simulations with the proposed methodology to design the model-based predictive air temperature controller are presented. The results indicate a better efficiency of the particle swarm optimisation algorithm as compared with the efficiencies obtained with a genetic algorithm and a sequential quadratic programming method.

Abstract
Modern greenhouse climate controllers are based on models in order to simulate and predict the greenhouse environment behaviour. These models must be able to describe indoor climate process dynamics, which are a function of both the control actions taken and the outside climate. Moreover, if predictive or feedforward control techniques are to be applied, it is necessary to employ models to describe and predict the weather. From all the climate variables, solar radiation is the one with greater impact in the greenhouse heat load. Hence, making good predictions of this physical quantity is of extreme importance. In this paper, the solar radiation is represented as a time-series and a support vector regression model is used to make long term predictions. Results are compared with the ones achieved by using other type of models, both linear and non-linear.

Abstract
A novel variant of a multi-objective particle swarm optimization algorithm is reported. The proposed multi-objective particle swarm optimization algorithm is based on the maximin technique previously proposed for a multi-objective genetic algorithm. The technique is applied to optimize two types of problems: firth to a set of benchmark functions and second to the design of PID controllers regarding the classical design objectives of set-point tracking and output disturbance rejection.

Abstract
The particle swarm optimization algorithm is proposed as a tool to solve the Posicast input command shaping problem. The design technique is addressed, in the context of a simulation teaching experiment, aiming to illustrate second-order system feedforward control. The selected experiment is the well known suspended load or gantry problem, relevant to the crane control. Preliminary simulation results for a quarter-cycle Posicast shaper, designed with the particle swarm algorithm are presented. Illustrating figures extracted from an animation of a gantry example which validate the Posicast design are presented.

Abstract
Modeling discrete event systems with sequential behavior can be a very hard and complex task. Some formalisms are used in this context, such as: Petri Nets, Statecharts, Finite automata, Grafcet and others. Among these, Grafcet seems to be a good choice because it is easy: to learn, to understand and to use. Teaching Grafcet is then relevant within engineering courses concerned with Industrial Automation. A virtual laboratory, e-GRAFCET, developed and first tested in UTAD University; it is a new, easy-to-use multimedia e-educational tool to support the self-learning process of Grafcet. This paper, reports a study of e-GRAFCET use by the students of University of Minho. A questionnaire was prepared and students asked to fulfill it in a volunteer basis. The results were statistically analyzed and the scores compared. The overall objective is to understand how the tool helps students in their study, and consequently improve their learning off Grafcet, independently of their engineering background.