Abstract
The technique of genetic algorithms is proposed as a means of auto-tuning PID controllers. The technique revolves firstly using on-line data and the genetic algorithm to identify a model of the process. Then the identified model, the genetic algorithm and simulation methods, are used to off-line tune the PID controller, so as to minimize a time-domain based cost function. Finally, the genetically tuned controller is implemented on-line on the real process. The results of the genetic auto-tuner are illustrated by auto-tuning a PID controller on a laboratory heat exchanger, and comparing the genetic auto-tuning technique with the Astrom-relay auto-tuning technique.

Abstract

Abstract
This paper describes the implementation of a distributed data acquisition network based on the 80C592 microcontroller from Intel. Each Station is connected in a hierarchical way to form a tree topology. The lower level network stations, designated by Slaves, are dedicate to the data acquisition and the generation of control signals. The upper level, Masters, are responsible for the communications control. Both networks uses a CAN - Controller Area Network - Bus, for Data Transferring, and the global Network is also connected to a PC, via CAN. A device router, NetManager, was implemented to support total intrinsic requirements at the communication level. This type of connection allows total configuration from a personal computer, PC, in which runs a software application developed for Windows(TM) environments. The tests performed at the laboratory, with transmission rates varying from 40Kbits/s to 1Mbits/s, showed that the communications were performed without errors for cable lengths of 1100m to 40m, respectively. This system is now being installed in a set of environmental chambers and greenhouses located on UTAD, where it will be monitored and controlled the air temperatures and humidities, the CO2 and ammonia concentrations and the radiation level.

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. Later, it was found that parameters change with varying operational conditions. Thus, for an efficient use of these models in real-time control, a recursive identification technique was implemented for the estimation of the parameters. Copyright (C) 1997 Elsevier Science Ltd.

Abstract
This paper describes the goals and some of the results obtained in the project entitled "Computerised Management of Greenhouses", that has started in November 1995. This project has a duration of three years and is sponsored by JNICT (Portuguese Institute for Scientific Research Funding). At present, the experimental set-up is composed of a greenhouse with a floor area of 210m(2) equipped with several actuators and sensors that are connected to an acquisition and control system developed at the Engineering Department of UTAD University. The set-points for the air temperature and humidity, CO2 concentration, soil moisture and ambient radiation inside the greenhouse are calculated on-line as a function of the greenhouse climate, outside weather and the specified optimal set of values for the plants, in this case tomato. The results show that the climate control system performs well and that has been achieved a reduction of the energy consumption compared with the case when the set-points are fixed. In the second year it will be implemented a distributed control system for monitoring and control the environment of four greenhouses located at the University campus. Also, in one of the greenhouses, will be used renewable energies such as the solar photovoltaic and the solar thermal energies. The first is to deliver power to the electronic equipment and to some of the actuators and the second for heating the greenhouses. During this period and the third year it will be implemented and compared different real-time control strategies with respect to set-point accuracy and energy consumption.

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.