Error-Driven Adaptive, Virtual Machine Model-Based Control with High Availability Platform

An error-driven adaptive model-based control system, for optimizing machine or assembly plant performance and operation under normal and fault conditions, is proposed. In such complex system it is imperative to differentiate between a system failure and a sensor failure or between process noise and measurement noise. In this paper, we present a comprehensive approach based on a hierarchical, multilevel control techniques. The approach is designed to provide sensor measurement validation, associates a degree of integrity with each measurement, identifies faulty sensors, and estimates the actual system states and sensor values in spite of faulty measurements. Using Virtual Machine Model concept, the method is achieved in three steps: state prediction, fault detection & sensor measurement and system online update or correction. A combination of flexible least square algorithm and adaptive Kalman filtering method are implemented to learn and predict system behavior. The experimental results show that the proposed model and algorithms can efficiently identify faulty components, reduce noise errors injected by sensors/system and thus providing self healing. The Virtual Machine Model (VMM) architecture described in this paper has proved to have several advantages over traditional models, the proposed model allows easy application provisioning, upgrades and maintenance, it provides fault tolerance, speedy disaster recovery and high availability platform.