Controllability, Observability in Networked Control

We reconsider and advance the analysis of structural properties (controllability and observability) of a class of linear Networked Control Systems (NCSs). We model the NCS as a periodic system with limited communication where the non updated signals can either be held constant (the zero-order-hold case) or reset to zero. Periodicity is dealt using the lifting technique. We prove that a communication sequence that avoids particularly defined pathological sampling rates and updates each actuator signal only once is sufficient to preserve controllability (and observability for the dual problem of sensor scheduling). These sequences can be shorter than previously established and we set a tight lower bound to them

Optimization approaches for controller and schedule codesign in networked control

We consider the offline optimization of a sequence for communication scheduling in networked control systems. Given a continuous-time Linear Quadratic Regulator (LQR) problem we design a sampled-data periodic controller based on the continuous time LQR controller that takes into account the limited communication medium and inter-sampling behavior. To allow for a Riccati equation approach, singularities in the weighting matrices and time-variance are accounted for using a lifting approach. Optimal scheduling can be obtained by solving a complex combinatorial optimization problem. Two stochastic algorithms will be proposed to find a (sub)optimal sequence and the associated optimal controller which is the result of a discrete algebraic Riccati equation for the given optimal sequence

Light bulb moments: identifying information research threshold concepts for fourth year engineering students

The librarians in the Dorothy Hill Physical Sciences and Engineering Library undertook a project to identify information research threshold concepts which fourth year undergraduate students must know to produce high quality research assignments. The methodology used to identify threshold concepts was to survey students, librarians and academics. A suggested threshold concept in information research is the critical evaluation of information resources to establish their authority, quality and credibility. This paper aims to demonstrate how a threshold concept approach clarifies the student experience in information research and provides a framework for the design of future information skills training

An adaptive observer-based parameter estimation algorithm with application to road gradient and vehicle's mass estimation

A novel observer-based parameter estimation algorithm with sliding mode term has been developed to estimate the road gradient and vehicle weight using only the vehicle's velocity and the driving torque from the engine. The estimation algorithm exploits all known terms in the system dynamics and a low pass filtered representation to derive an explicit expression of the parameter estimation error without measuring the acceleration. The proposed algorithm which features a sliding-mode term to ensure the fast and robust convergence of the estimation in the presence of persistent excitation is augmented to an adaptive observer and analyzed using Lyapunov Theory. The analytical results show that the algorithm is stable and ensures finite-time error convergence to a bounded error even in the presence of disturbances. A simple practical method for validating persistent excitation is provided using the new theoretical approach to estimation. This is validated by the practical implementation of the algorithm on a small-scaled vehicle, emulating a car system. The slope gradient as well as the vehicle's mass/weight are estimated online. The algorithm shows a significant improvement over a previous result

Improving Data Infrastructure to Reduce Firearms Violence

In the fall of 2020, Arnold Ventures, a philanthropy dedicated to maximizing opportunity and minimizing injustice, and NORC at University of Chicago, an objective nonpartisan research institution, released the Blueprint for a US Firearms Infrastructure (Roman, 2020). The Blueprint is the consensus report of an expert panel of distinguished academics, trailblazing practitioners, and government leaders. It describes 17 critical reforms required to modernize how data about firearms violence of all types (intentional, accidental, and self-inflicted) are collected, integrated and disseminated. This project, which is also supported by Arnold Ventures, takes the conceptual priorities described in the Blueprint and proposes specific new steps for implementation.The first step in building a better firearms data infrastructure is to acknowledge where we currently stand. In The State of Firearm Data in 2019 (Roman, 2019), the expert panel found that while there are a substantial number of data sources that collect data on firearms violence, existing datasets and data collections are limited, particularly around intentional injuries. There is some surveillance data, but health data on firearms injuries are kept separately from data on crimes, and there are few straightforward ways to link those data. Data that provide context for a shooting--where the event took place, and what the relationship was between victim and shooter--are not available alongside data on the nature of injuries. Valuable data collections have been discontinued, data are restricted by policy, important data are not collected, data are often difficult to access, and contemporary data are often not released in a timely fashion or not available outside of specialized settings. As a result, researchers face vast gaps in knowledge and are unable to leverage existing data to build the evidence base necessary to adequately answer key policy questions and inform firearms policymaking.In the Blueprint, the expert panel developed a set of recommendations organized around a reconceptualization of how data are collected and who collects data. The broad themes from the Blueprint are as follows:Almost all surveillance data in health and criminal justice is generated locally. It is a high priority to provide information, technical assistance, implementation supports, and funding to state and local governments to improve their collections.Comprehensive monitoring of all federal data collections is needed to ensure that important data elements are being collected, data gaps are being addressed, and quality issues are quickly resolved.Timely dissemination of key data is important, including the development of guidelines to ensure consistency across collections and that resources are made available to speed reporting for collections with historical delays.Improvement is needed in strategic communication about the purpose and use of data to federal agencies, researchers and to the general public.The current report builds on the Blueprint by developing implementation guidance for key recommendations. Where the Blueprint included actionable recommendations, such as naming discontinued surveys that should be resurrected, this report develops specific recommendations for implementation. The report is centered on three topics that were the highest priority for the expert panel but that required additional research before guidance could be disseminated. The research findings from that additional investigation are reported here, and recommendations to facilitate implementation are described. The three topic areas are as follows:The creation of a nonfatal firearms injury databaseIncreasing the quality, availability, and usefulness of firearms data for research and policyPractical steps for building state capacity and infrastructure to use data for evidence-based decision-makin

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.Comment: 23 pages, 3 figures, Accepted in IEEE Transactions on Intelligent Transportation System