Network-Induced Delay Models for Can-Based Networked Control Systems Evaluation-Edición Única

Abstract

Networked Control Systems (NCS) are a variation of traditional Point-to-Point control systems. In NCS, sensors and actuators may be physically distributed and a serial common-bus communication network is used to exchange system information and control signals. Because all components use the same communication network, network-induced delays make the system stochastic and hard to predict. The Quality of Control (QoC) of each closed-loop system in a NCS is strongly affected by the network-induced delay produced by sensors and control signals. Controller Area Network (CAN) is a popular real-time field-bus used for small-scale distributed environments such as automobiles, and recently in aircraft and aerospace electronics, medical equipment, and factory and building automation. In CAN, the time delay exhibits a stochastic behavior and varies according to the network load. Since QoC is affected by delays, designing and evaluating a controller must take into account the effect of network-induced delays. This thesis illustrates two models that play the role of classifiers and estimators for network-induced delays. Based on experimental delay measurements, the models can estimate the network load and predict future time delay values. The models were built following a statistical approach using a continuous Hidden Markov Model, and a histrogram-based approach. They were trained/tested using experimental data taken from a real CAN system with excellent results. The CAN system used to perform the experiments is a multiplexed CAN scale model from EXXOTest R , which is a training unit with real components of a Peugeot 807. In addition, two examples of the applicability of the models are illustrated. A NCS simulator for evaluating systems under different network conditions, and a NCS observer-based controller. The results for both applications show excellent performance, especially in high network loads.