Performance analysis with network-enhanced complexities: On fading measurements, event-triggered mechanisms, and cyber attacks

Copyright © 2014 Derui Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Nowadays, the real-world systems are usually subject to various complexities such as parameter uncertainties, time-delays, and nonlinear disturbances. For networked systems, especially large-scale systems such as multiagent systems and systems over sensor networks, the complexities are inevitably enhanced in terms of their degrees or intensities because of the usage of the communication networks. Therefore, it would be interesting to (1) examine how this kind of network-enhanced complexities affects the control or filtering performance; and (2) develop some suitable approaches for controller/filter design problems. In this paper, we aim to survey some recent advances on the performance analysis and synthesis with three sorts of fashionable network-enhanced complexities, namely, fading measurements, event-triggered mechanisms, and attack behaviors of adversaries. First, these three kinds of complexities are introduced in detail according to their engineering backgrounds, dynamical characteristic, and modelling techniques. Then, the developments of the performance analysis and synthesis issues for various networked systems are systematically reviewed. Furthermore, some challenges are illustrated by using a thorough literature review and some possible future research directions are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 61203139, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

INTELLIGENT DEMAND SIDE MANAGEMENT OF RESIDENTIAL BUILDING ENERGY SYSTEMS

Building energy performance has emerged as a major issue in recent years due to growing concerns over costs, resource limitations, and the potential impact on climate. According to the 2011 Buildings Energy Data Book (prepared by D&R International, Ltd. for the US Department of Energy, March 2012), the built environment demands about 41% of primary energy in the United States [1]. Given the emergence of modern sensing technologies and low-cost data-processing capabilities, there is a growing interest in better managing and controlling consumption within buildings. Estimates suggest that the simple act of continuous monitoring can lead to improvements on the order of 20% [118]. To further reduce and better control energy consumption, one can turn to the use of real-time energy-performance modeling. This thesis adopts the view that smaller buildings (i.e. homes and smaller commercial facilities), which represent more than half of the sector’s consumption, provide a rich opportunity for low-cost monitoring solutions. The real advantage lies in the growth of so-called smart meters for demand monitoring and advanced sensing for improved load control. In particular, this thesis considers the use of a small sensor package for the creation of autonomously developed, data-driven thermal models. Such models can be used to predict and control the consumption of space heating and cooling equipment, which currently represent about 50% of residential consumption in the United States. The key contribution of this work is the real-time identification of thermal parameters in homes using only two temperature sensors, solar irradiance measurements, and a power meter with load-tracking capabilities. The proposed identification technique uses the Prediction Error Method (PEM) to find a Multiple Input Single Output (MISO) state-space model. Two different models have been devised, and both have been field tested. The first focuses on energy forecasting and enables various diagnostic features; the other is formulated for more advanced capacity controls in vapor-compression air conditioners. A Model Predictive Control (MPC) scheme has been implemented and shown in simulation to yield energy reductions on the order of 30% over typical thermostatic control schemes. Similarly, the diagnostic model has been used to detect capacity degradation in operational units

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

Maximum Likelihood Estimation in Data-Driven Modeling and Control

Recently, various algorithms for data-driven simulation and control have been proposed based on the Willems' fundamental lemma. However, when collected data are noisy, these methods lead to ill-conditioned data-driven model structures. In this work, we present a maximum likelihood framework to obtain an optimal data-driven model, the signal matrix model, in the presence of output noise. Data compression and noise level estimation schemes are also proposed to apply the algorithm efficiently to large datasets and unknown noise level scenarios. Two approaches in system identification and receding horizon control are developed based on the derived optimal estimator. The first one identifies a finite impulse response model. This approach improves the least-squares estimator with less restrictive assumptions. The second one applies the signal matrix model as the predictor in predictive control. The control performance is shown to be better than existing data-driven predictive control algorithms, especially under high noise levels. Both approaches demonstrate that the derived estimator provides a promising framework to apply data-driven algorithms to noisy data