Copyright 0 IF AC Control Applications and Ergonomics in Agriculture, Athens, Greece, 1998 A KNOWLEDGE BASED SCADA SYSTEM FOR AGRICULTURAL PROCESS CONTROL A. Anastasiou, N.Rerras, N. Sigrimis, Dept. of Agricultural Engineering Agricultural University of Athens, Athens 11855, Greece e-mail: ns@auadec.aua.gr Abstract: This approach leads to the design of a digital control system with generalized functions for process control, configurable to meet greenhouse control requirements. Intelligence is shared among low level control loops in the controller and high level decisions made at the central process computer. The system can operate in "central mode", "remote control" or "hybrid mode". The development of an open Knowledge Based System in the form of tasks and subtasks, provides an elegant way of rapid program development. New rules can be added at any time and new control scenarios may be implemented by adding new tasks. Fuzzy decisions and fuzzy controllers at the supervisory level provide adaptive reference (set-point) generators, which are key elements in optimal greenhouse control. At the low level, the adaptive reference generators enable the realization of optimality criteria in real-time, based on maximizing the "gain" when possible, and minimizing the "expenditure" based on a sequential estimate of plant's reserves. The system is also equipped with an on-line process optimization tool, which was built based on a modified search algorithm with accelerated learning. Each process when entered under the optimizing monitor, is performance driven, conducts real experiments on the site and uses a modified descent method to maximize performance. An adaptive system guides the selection of control parameter values on-line, in a process searching for the global minimum of the cost function. A mathematical model of the process is not necessary when performance can be estimated by measurements of conducted experiments. This paper presents the general features of the system, how it can be set-up to achieve specific goals and discusses results of tests conducted for energy saving during greenhouse heating. Copyright @1998, IFAC Keywords : optimization, intelligence, process control, knowledge based system 1. INTRODUCTION Researchers report an overwhelming satisfaction in implementing pioneer expert systems for the energy and general management of greenhouses or other agricultural processes. Nevertheless the number of actual implementations of such systems in real practice remains small compared to the number of systems successfully demonstrated in prototype form. This discrepancy suggests that there are concerns about the cost and difficulty of integrating expert systems into a Greenhouse Management System (GMS). Applications must address this problem before the technology finds wider acceptance. 163 Expert control systems solve problems in a heuristic way, they interact with the user easily, gain experience from past records and can give answers to nonlinear, or imprecisely stated, problems. They combine the advantages of adaptive controllers (Astrom, 1988) with the ability to explore and reason about selection of control strategies and assess control performance. Part of this effort was to determine why integrating an expert system or other AI applications in a GMS is more complex than adding a conventional application. Based on such an analysis the interfaces needed to achieve an effective integration were defined. This groundwork led