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.

Abstract
Artificial co-evolutionary techniques are proposed in a new and novel paradigm to solve the problem of designing a robust fixed PID controller for a plant with prescribed plant uncertainties. The co-evolutionary scheme used, involves generating two separate populations, one representing the controller and the other the plant. Two separate cost functions are then used in the co-evolutionary scheme to reflect the different goals of the two populations. The two populations are then co-evolved such that the population of plants, with the prescribed uncertainties, contains the set of difficult plants to control and a population of controllers emerges, which can control all these difficult plants effectively. The resulting paradigm not only results in a robust controller design but also produces a set of worst case plants. This co-evolutionary approach is illustrated through co-evolving a PID controller for a linear plant which has a set of prescribed uncertainties.

Abstract
This paper describes the implementation of a wireless data acquisition network for agricultural applications based on the Microchip PIC16C71, and the Intel 87C592 microcontrollers. The system uses a set of solar powered wireless data-acquisition stations (SPWAS) linked by radio frequency to a base station (BS). The base station has as main functions the control of the data-acquisition stations and the storage of the data collected by them. The network has been applied to collect outdoor and indoor climate data from a set of greenhouses located at Universidade de Tras-os-Montes e Alto Douro (UTAD), in the northwest of Portugal. Experimental tests are being done since mid June 1996, and the results obtained shows that the communications to the BS station were performed without errors and no loss of data.

Abstract
This paper describes a computer-based temperature control system for a growth chamber. The system consists of a low cost computer with a data acquisition card acting as the interface between the process and the computer. The software developed has a menu-driven user friendly interface to access execute the functions implemented: choice of the sampling interval, choice of the setpoints, name files, etc.. The user can program up to six different temperature setpoints as constituting six time programmable events along the day. The identification of the thermal process was made from the analysis of the output data collected from the process in response to random generated input signals. Using this model it was possible to establish optimal linear and non linear control strategies in order to achieve the desired setpoint and to minimize the number of actuation. The actuation over the heating and cooling systems were performed with conventional PID and fuzzy controllers and their responses were compared.

Abstract
In this paper climate discrete-time dynamic models for the inside air temperature of two different greenhouses are identified, using data acquired during two different periods of the year. These models employ data from air temperature and relative humidity (both inside and outside the greenhouse), solar radiation, wind speed and control inputs (ventilation, etc.), acquired with a sampling rate of 30s and storage rate of 1 min. Using software developed with Matlab, and its Identification Toolbox, the structure of the dynamic transfer functions is first selected, and its parameters afterwards identified using off-line techniques. Several simulations experiments show that the second-order models identified achieve a close agreement between simulated and experimental data. As different model parameters were obtained for each case studied, implying that the system is time-varying, these models must be obtained in real-time, employing on-line identification techniques. These models will afterwards be used in real-time climate control, where different control strategies will be compared, with respect to set-point accuracy, energy consumption and actuator wear.