Abstract
This paper describes a computer-based temperature control system for an environmental chamber. The identification of the thermal process was made from the analysis of the collected output data in response to random generated input signals. Using this model it was established the optimal linear and non linear control strategies in order to achieve the desired set-point and to minimize the number of actuations. The actuation over the heating and cooling systems were performed with conventional PID and fuzzy controllers and their responses were compared. This work enhances fuzzy controllers application with self-learning capability to achieve the prescribed control objectives in a near-optimal manner. By applying back-propagation through time it was possible to force the plant block to generate the desired trajectory.

Abstract
A real-time parameter estimator for the climate discrete-time dynamic models of a greenhouse located at the North of Portugal are presented. The experiments showed that the second-order models identified for the air temperature and humidity achieve a close agreement between simulated and experimental data. The real-time data acquisition and the recursive identification techniques implemented are used in the simulation and design of an adaptive climate controller to achieve set-point accuracy and minimisation of the energy inputs.

Abstract
For a greenhouse located at UTAD-University, the methods used to estimate in real-time the parameters of the inside air temperature model will be described. The structure and the parameters of the climate discrete-time dynamic model were previously identified using data acquired during two different periods of the year. Several experiments showed that the second-order models identified achieve a close agreement between simulated and experimental data. Afterwards it has been found that parameters change with varying operational conditions. Thus, for a efficient use of these models in real-time control a recursive identification technique for the estimation of the parameters was implemented.

Abstract
The control of manufacturing processes requires the use of a communication infrastructure. Since the object-oriented model is well adapted for the modelling of industrial processes, an ORE seems the logical choice for the communication layer. This paper presents an ORE developed for this purpose, based on DCE services.

Abstract
A major public transport company, working in a urban environment, in Portugal, has adapted a telemetric system for automatic vehicle location, AVL, in order to optimise vehicle use, timetabling and scheduling. The system is able to provide real time location and load of the buses, to exchange voice messages with the driver and to propose preventive maintenance actions on the vehicles. The control centre exchanges messages with all the buses. These messages have two different components: the real-time control component, that is thrown away after use, and the off-line component that has its data stored sequentially on disk, far later analysis. Our main concern is to extract relevant information from this bulk of data to provide the bus management expert with a more accurate knowledge of the bus fleet performance, namely the bus operation efficiency, to improve vehicle use and scheduling. Several factors may corrupt the data that is stored on disk and make it impossible to automatically extract useful information. A pre-processing stage is needed to classify data as consistent or inconsistent. A strategy to implement this preprocess stage has been proposed in a previous paper [Cunha 1997], The idea is based on a virtual bus model. The virtual bus travels on a bus route and recreates the real bus service. It compares messages which have been received with those that could be expected in the model. The model is extended in this paper, in order to analyse inconsistent data and take automatic correction actions. In less common situations, control is passed to a human operator for him/her to make an appropriate correction.

Abstract
The technique of genetic algorithms (GAs) is proposed as a means of auto-tuning PI Smith predictor controllers. The technique involves firstly using on-line data and the GA to identify a model of the process. Then the identified model, the GA and simulation methods, are used to off-line tune the PI Smith predictor controller, so as to minimise a time-domain based cost function. Finally, the genetically tuned controller is implemented on-line on the real process. The results of the auto-tuning of the PI Smith predictor controller technique are illustrated on a laboratory heat exchanger, and a comparison between this technique, and both the genetic PI auto-tuning technique and the Astrom PI auto-tuning technique are made.