A Knowledge Based Scada System for Greenhouse Automation

Abstract

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. The system can operate in "central mode", "remote control" or "hybrid mode". The overall system consists of many different component processes, with strong or week coupling, which must be viewed and served hierarchically. The overall system to be optimized is a very complex large scale system, with many parallel processes and events, and needs a powerful SCADA system, with control and management components.This paper presents the general features of the system, how it can be set-up to achieve specific goals and discusses the features of the KBS subsystem.