Analysis of Embedded Controllers Subject to Computational Overruns

Microcontrollers have become an integral part of modern everyday embedded systems, such as smart bikes, cars, and drones. Typically, microcontrollers operate under real-time constraints, which require the timely execution of programs on the resource-constrained hardware. As embedded systems are becoming increasingly more complex, microcontrollers run the risk of violating their timing constraints, i.e., overrunning the program deadlines. Breaking these constraints can cause severe damage to both the embedded system and the humans interacting with the device. Therefore, it is crucial to analyse embedded systems properly to ensure that they do not pose any significant danger if the microcontroller overruns a few deadlines.However, there are very few tools available for assessing the safety and performance of embedded control systems when considering the implementation of the microcontroller. This thesis aims to fill this gap in the literature by presenting five papers on the analysis of embedded controllers subject to computational overruns. Details about the real-time operating system's implementation are included into the analysis, such as what happens to the controller's internal state representation when the timing constraints are violated. The contribution includes theoretical and computational tools for analysing the embedded system's stability, performance, and real-time properties.The embedded controller is analysed under three different types of timing violations: blackout events (when no control computation is completed during long periods), weakly-hard constraints (when the number of deadline overruns is constrained over a window), and stochastic overruns (when violations of timing constraints are governed by a probabilistic process). These scenarios are combined with different implementation policies to reduce the gap between the analysis and its practical applicability. The analyses are further validated with a comprehensive experimental campaign performed on both a set of physical processes and multiple simulations.In conclusion, the findings of this thesis reveal that the effect deadline overruns have on the embedded system heavily depends the implementation details and the system's dynamics. Additionally, the stability analysis of embedded controllers subject to deadline overruns is typically conservative, implying that additional insights can be gained by also analysing the system's performance

Networked Control System Design and Parameter Estimation

Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6). Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of H₂ and H∞ norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed H₂/H∞ control problem is solved under the framework of LMIs. To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The l₂-l∞ filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. Finally, several open problems are listed as the future research directions

Synchrophasor Data Analytics for Control and Protection Applications in Smart Grids

RÉSUMÉ Des réseaux intelligents sont des réseaux d’énergie fortement distribués où les technologies d’énergie et des services sont intégrés avec des informations, des communications et contrôlent des technologies. Puisque les sources d’énergie renouvelable deviennent plus efficaces et rentables, les réseaux intelligents peuvent livrer la puissance propre, durable, sécuritaire, et fiable aux consommateurs. Cependant, l’utilisation rapide de sources d’énergie renouvelable provoque des défis techniques en termes de surveillance, le contrôle et la protection des réseaux électriques. En fait, l’énergie renouvelable implique les phénomènes qui sont naturellement stochastiques comme la lumière du soleil et le vent. Donc, les réseaux intelligents devraient être capables de surveiller et répondre aux changements tant dans fournisseur d’énergie que dans la demande. L’évolution des réseaux électriques provoque aussi le déploiement de nombreuses unités de mesure sans précédent et d’intelligents appareils de mesure. En vertu des systèmes de communications, les signaux en temps réel et les données peuvent être échangés entre les composants des réseaux intelligents. Le flux de données en temps réel fournit une occasion unique pour des applications axées sur les données et des outils pour démultiplier la modernisation de réseaux et la résilience. Les unités de mesure de phaseur sont les dispositifs spécialisés qui acquièrent le phaseur synchronisé (synchrophasor) des données des réseaux électriques. L’analytique de données Synchrophasor peut potentiellement étre plus performant que des méthodes traditionnelles en termes de prise de décisions. Spécifiquement, l’analytique de données est des approches qualitatives/quantitatives et les algorithmes qui rassemblent et traitent des données pour en fin de compte améliorer la conscience situationnelle dans des réseaux électriques. Motivé par ce fait, cette thèse présente des solutions viables pour l’analytique de données synchrophasor dans le but d’améliorer la surveillance, le contrôle et la protection de réseaux de distribution. La thèse se concentre sur trois fonctionnalités qui sont portées de basé sur l’analytique de données synchrophasor: Détection de perturbation centralisée, surveillance de production décentralisée (PD) et la protection “backup” coordonnée. L’objectif de surveillance de perturbation est de réaliser la détection rapide et fiable de tension/des déviations de fréquence qui affectent la stabilité de réseau. La surveillance de PD est liée à la détection de la présence/absence de ressources énergétiques pour la gestion du flux de puissance.----------ABSTRACT Smart grids are highly distributed energy networks where energy technologies and services are integrated with information, communications and control technologies. As renewable energy sources are becoming more efficient and cost–effective, the smart grids can deliver safe, clean, sustainable and reliable power to consumers. However, the rapid utilization of renewable energy sources brings about technical challenges in terms of monitoring, control, and protection of power systems. In fact, renewable energy involves phenomena which are naturally stochastic such as sunlight and wind. Therefore, the smart grids should be capable of monitoring and responding to changes in both power supply and demand. The evolution of the power systems also gives rise to deployment of unprecedented number of measurement units and smart meters. By virtue of communications systems, real-time signals and data can be exchanged between components of the smart grids. The flow of real-time data provides a unique opportunity for data-driven applications and tools to leverage grid modernization and resiliency. Phasor measurement units are specialized devices that acquire synchronized phasor (synchrophasor) data from the power systems. Synchrophasor data analytics can potentially outperform traditional methods in terms of decision making. Specifically, data analytics are qualitative/quantitative approaches and algorithms that collect and process data to ultimately improve situational awareness in the power systems. Motivated by this fact, this thesis presents viable solutions for synchrophasor data analytics with the aim of improving monitoring, control and protection of power distribution grids. The thesis focuses on three functionalities that are carried out based on synchrophasor data analytics: Centralized disturbance detection, monitoring of distributed generation (DG) systems, and coordinated backup protection. The objective of disturbance monitoring is to achieve fast and reliable detection of voltage/frequency deviations that affect the network stability. The DG monitoring is concerned with detecting presence/absence of energy resources for management of the flow of power. Disturbance and DG monitoring tools pave the way for adaptive backup protection of active distribution networks. The adaptive backup protection scheme ensures the post-fault stability by detecting line faults within a permissible tolerance time. The coordination between control and backup protection systems leads to fast recovery of voltage/frequency and minimizes power outage. The efficacy and reliability of the developed methods and algorithms are validated by extensive computer simulations based on different benchmarks

Development and Control of Networked Servo System

Control systems where the control loops are closed through a communication network are called Networked Control System (NCS). Research on NCS has received increased attention in recent years due to the advancement of control,computation and communication technologies. NCS makes the design and implementation of control systems with reduced complexity due to simpler installation and easy maintenance. But the insertion of the communication network in the feedback control loop introduces delay from sensor to controller and controller to actuator, that degrades the control system performance and also causes system instability.This thesis focuses on development of a networked DC Servo control system using LabVIEW and Peripheral Component Interconnect(PCI) card.The controller design for a NCS can be categorized into indirect and direct approach. An indirect approach controller design considers ¯rst without delay followed by design a suitable delay compensation technique. A PID controller with a Smith predictor as a compensater is implemented in real-time networked control of servo system. The above PID controller is tuned using gain margin and phase margin speci¯cations and Zigler-Nichols method are implemented. A direct NCS design approach in the other hand considers the delay as well as packet loss characteristics with system dynamics at one go.This approach gives more information about each instant of the system.It uses Lyapunov approach to design of asymptomatic stabilization of the system, the above stabilization uses a switched approach for NCS sta- bilization with packet loss and delay is proposed. The switched approach divides the NCS as di®erent subsystems considering both delay and packet loss, then designing of controllers for each subsystem. According to packet loss, the subsystems and controllers are switched to stabilize the NCS. In this approach the feedback gains are calculated by solving Linear Matrix Inequalities (LMIs). Both direct and indirect controller design approach are simulated using MATLAB and SIMULINK. Some Hardware in Loop simulations are also performed on a Servo System.A realtime networked servo control system has been developed using LabVIEW. Only indirect controller approach is implemented in this environment to remotely control the servo system. The results obtained by using PID controller and Smith predictor have been analyzed and it is conrmed that these controller provide good performances

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Distributed Kalman Filters over Wireless Sensor Networks: Data Fusion, Consensus, and Time-Varying Topologies

Kalman filtering is a widely used recursive algorithm for optimal state estimation of linear stochastic dynamic systems. The recent advances of wireless sensor networks (WSNs) provide the technology to monitor and control physical processes with a high degree of temporal and spatial granularity. Several important problems concerning Kalman filtering over WSNs are addressed in this dissertation. First we study data fusion Kalman filtering for discrete-time linear time-invariant (LTI) systems over WSNs, assuming the existence of a data fusion center that receives observations from distributed sensor nodes and estimates the state of the target system in the presence of data packet drops. We focus on the single sensor node case and show that the critical data arrival rate of the Bernoulli channel can be computed by solving a simple linear matrix inequality problem. Then a more general scenario is considered where multiple sensor nodes are employed. We derive the stationary Kalman filter that minimizes the average error variance under a TCP-like protocol. The stability margin is adopted to tackle the stability issue. Second we study distributed Kalman filtering for LTI systems over WSNs, where each sensor node is required to locally estimate the state in a collaborative manner with its neighbors in the presence of data packet drops. The stationary distributed Kalman filter (DKF) that minimizes the local average error variance is derived. Building on the stationary DKF, we propose Kalman consensus filter for the consensus of different local estimates. The upper bound for the consensus coefficient is computed to ensure the mean square stability of the error dynamics. Finally we focus on time-varying topology. The solution to state consensus control for discrete-time homogeneous multi-agent systems over deterministic time-varying feedback topology is provided, generalizing the existing results. Then we study distributed state estimation over WSNs with time-varying communication topology. Under the uniform observability, each sensor node can closely track the dynamic state by using only its own observation, plus information exchanged with its neighbors, and carrying out local computation