Transmissibility operators for state and output estimation in nonlinear systems

Transmissibility operators are mathematical objects that characterize the relationship between two subsets of responses of an underlying system. The importance of transmissiblity operators comes from the fact that these operators are independent on the system inputs. This work develops the transmissibility theory for nonlinear systems for the first time. The system nonlinearities are assumed to be unknown, which gives a wide range of possible engineering applications in different disciplines. Four different methods are developed to deal with these nonlinearities. The first method is by re-constructing the system nonlinearities as independent excitations on the system. This method handles the inherent unmodeled nonlinearities within the system. The second method is by designing a transmissibility-based sliding mode control. This method rejects unwanted nonlinearities such as system faults. The third method is by constructing the system as time-variant linear system, and use recursive least squares to solve it. This method can handle nonlinear systems with time-variant dynamics. The fourth method is by designing a new robust estimation technique called high-gain transmissibility (HGT) that is inspired by high-gain observers. This estimator has the ability to robustly estimate the system states in a high-gain form. The majority of modern fault detection, control systems, and robots localization depend on mathematically estimating the system states and outputs. Transmissibility-based estimation is incorporated in this work with these three theoretical applications. For fault detection, transmissibility operators are used along a set of outputs to estimate the measurements of another set of outputs. Then faults are detected by comparing the estimated and measured outputs with each other. Control approaches use the transmissibility-based estimation to construct the control signal, in which is injected back to the original system. Robots localization fuses the transmissibility-based estimation with real-time sensor measurements to minimize the error in determining the robot displacements. These three theoretical applications are applied on four different systems. The first system is Connected Autonomous Vehicles (CAV) platoons. A CAV platoon is a network of connected autonomous vehicles that communicate together to move in a specific path with the desired velocity. Transmissibilities are proposed along with the measurements from sensors available in CAV platoons to identify transmissibility operators. This will be then developed to mixed autonomous and human-driven vehicle platoons. Besides the wide range of physical and cyber faults in such systems, this is also motivated by the fact that on-road human-drivers’ behaviour is unknown and difficult to be predicted. Transmissibility operators are used here to handle both cyber-physical faults as well as the human-drivers’ behaviour. The platoon faults are then proposed to be mitigated using a transmissibility-based sliding mode controller. Moreover, transmissibilities are integrated with Kalman filter to localize CAV platoons while operating under non-Gaussian environment as unknown nonlinearities. The second system is a multi-actuator micro positioning system that is used in the semi-conductors industry. Transmissibility operators are applied on this system for fault detection and fault-tolerant control. Fault detection is represented in applying the proposed developments to actuator fault detection. Some of the most common actuator faults such as actuator loss of effectiveness and fatigue crack in the connection hinges will be considered. Transmissibilities then will be used for fault detection without knowledge of the dynamics of the system or the excitation that acts on the system. Next, a transmissibility-based sliding mode control will be implemented to mitigate common actuator faults in multi-actuator systems. The third system is flexible structures subjected to unknown and random excitations. Structures used in applications subjected to turbulent fluid flow such as aerospace and underwater applications are subjected to random excitations distributed along the structure. Transmissibility operators are used for the purpose of structural fault detection and localization during the system operation. The fourth system is robotic manipulators with bounded nonlinearities and time-variant parameters. Both parameter variation and system nonlinearities are considered to be unknown. Transmissibility operators are integrated with Recursive Least Squares (RLS) to overcome the unknown variant parameters. RLS identifies transmissibilities used in the structure of noncausal FIR (Finite Impulse Response) models. While parameter variation can be treated as system nonlinearities, the RLS algorithm is used to optimize what time-variant dynamics to include in the transmissibility operator and what dynamics to push to the system nonlinearities over time. The identified transmissibilities are then used for the purpose of fault detection in an experimental robotic arm with variant picked mass

Stability analysis of a stochastic port-Hamiltonian car-following model

Port-Hamiltonian systems are pertinent representations of many non-linear physical systems. In this article, we formulate and analyse a general class of stochastic car-following models having a systematic port-Hamiltonian structure. The model class is a generalisation of classical car-following approaches, including the Optimal Velocity model by Bando et al. (1995), the Full Velocity Difference model by Jiang et al. (2001), and recent stochastic following models based on the Ornstein-Uhlenbeck process. In contrast to traditional models for which the interaction is totally asymmetric (i.e., depending only on the speed and distance to the predecessor), the port-Hamiltonian car-following model also depends on the distance to the follower. We determine the exact stability condition of the finite system with $N$ vehicles and periodic boundaries. The stable system is ergodic with a unique Gaussian invariant measure. Other model properties are studied using numerical simulation. It turns out that the Hamiltonian component improves the flow stability, reducing the total energy in the system. Furthermore, it prevents the problematic formation of stop-and-go waves with periodic dynamics, even in the presence of stochastic perturbations.Comment: 23 pages, 4 figure

Strategic and Tactical Guidance for the Connected and Autonomous Vehicle Future

Autonomous vehicle (AV) and Connected vehicle (CV) technologies are rapidly maturing and the timeline for their wider deployment is currently uncertain. These technologies are expected to have a number of significant societal benefits: traffic safety, improved mobility, improved road efficiency, reduced cost of congestion, reduced energy use, and reduced fuel emissions. State and local transportation agencies need to understand what this means for them and what they need to do now and in the next few years to prepare for the AV/CV future. In this context, the objectives of this research are as follows: Synthesize the existing state of practice and how other state agencies are addressing the pending transition to AV/CV environment Estimate the impacts of AV/CV environment within the context of (a) traffic operations—impact of headway distribution and traffic signal coordination; (b) traffic control devices; (c) roadway safety in terms of intersection crashes Provide a strategic roadmap for INDOT in preparing for and responding to potential issues This research is divided into two parts. The first part is a synthesis study of existing state of practice in the AV/CV context by conducting an extensive literature review and interviews with other transportation agencies. Based on this, we develop a roadmap for INDOT and similar agencies clearly delineating how they should invest in AV/CV technologies in the short, medium, and long term. The second part assesses the impacts of AV/CVs on mobility and safety via modeling in microsimulation software Vissim

Deep Learning Based Malware Classification Using Deep Residual Network

The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Proceedings, MSVSCC 2019

Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019. The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference. Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference. Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere

Proceedings, MSVSCC 2012

Proceedings of the 6th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2012 at VMASC in Suffolk, Virginia

Decentralized Multiagent Coordination for Connected and Autonomous Vehicle Routing in Congested Networks

In 2017, the cost of congestion in the United States was around 305 billion dollars, and city-dwellers, on average, lost 1400 dollars while sitting 42 hours in traffic jams. Aiming for better mobility and more efficient utilization of the transportation network, emerging connected and autonomous vehicle (CAV) technologies and their communication capabilities can produce well-coordinated and more efficient routing behavior to dissipate the traffic rather uniformly throughout the network, resulting in slower travel times. Vehicle routing is among the most critical and challenging, yet unsolved, tasks in CAV research. Current routing strategies either rely on a centralized control system which can fail in scaling, or employ decentralized schemes that yield sub-optimal coordination and poor system performance. In addition, it is of great importance for the deployment of CAV technologies to understand the transportation systems behavior in a mixed environment with various levels of communication complexity, where CAVs and Non-CAVs coexist and interoperate. The routing problem in a multiagent system resembles a competitive congestion game. The decisions of one agent (in this case, a CAV) directly impacts the performance of the others. When the number of agents traversing the same transportation facility at the same time exceeds a certain threshold, bottlenecks may occur, and thus, higher travel times. Therefore, coordination between CAVs is key to avoiding such circumstances. This dissertation answers how and to what extent different routing optimization algorithms, under various levels of autonomy and communication capabilities, can increase the mobility of the transportation system. This work designs this system in a decentralized manner that scales linearly in achieving a social and system-level optimum. To realistically analyze this system, we investigated the coordination behavior of CAVs under (1) No Communication, (2) Minimal Communication, and (3) Extensive Communication. In the absence of connectivity between the CAVs, a learning-based approach has been implemented where each CAV optimizes its own route using a reinforcement learning technique and based on its prior experiences. This competitive game quickly overwhelms the system as the market penetration of CAVs surpasses the critical threshold range (50% to 75%), where the mobility improvements are the most significant, and beyond which the system performance degrades. Under minimal communication level, we assumed the CAVs share information regarding their location and speed with the rest of the CAVs in their communication cluster through a multi-hop network. Then, a coordination scheme was implemented where each CAV minimizes its travel time based on the limited information it receives. Results showed that this application can reduce system travel time by up to 20%. Additionally, the emergence of mobility benefits are shown to correlate with the CAV network characteristics through the lens of percolation theory. The results revealed that, for the mobility benefits to surface, at least 70% of the CAVs are required to form a communication cluster. Under an extensive communication capability, where the CAVs not only share their location and speed but also their preferred path to their destination, a reduction of up to 40% in system travel time was achieved for high levels of CAV market penetration and communication radius. Moreover, the improvement in mobility was proved to be highly associated with the uniform dissipation of traffic onto the network. These findings provide solid support to create evidence-driven frameworks to guide future CAV development and deployment in a decentralized and coordinated manner

Flow-based Adaptive Split Signal Control

Over the last 35 years many adaptive traffic signal control systems have been developed presenting alternative strategies to improve traffic signal operations. However, less than 1% of all traffic signals in the United States are controlled by adaptive systems today. The extensive infrastructure necessary including reliable communication and complex calibration leads to a time consuming and costly process. In addition, the most recent National Traffic Signal Report Card indicated an overall grade of D for the nation’s traffic signal control and operations. Recent economic adversity adds to the already difficult task of proactively managing aged signal timing plans. Therefore, in an attempt to escape the status quo, a flow based adaptive split signal control model is presented, having the principal objective of updating the split table based solely on real-time traffic conditions and without disrupting coordination. Considering the available typical traffic signal control infrastructure in cities today, a non centralized system is proposed, directed to the improvement of National Electrical Manufacturers Association (NEMA) based systems that are compliant with the National Transportation Communications for Intelligent Transportation System Protocol (NTCIP) standards. The approach encompasses the User Datagram Protocol (UDP) for system communication allowing an external agent to gather flow information directly from a traffic signal controller detector status and use it to better allocation of phase splits. The flow based adaptive split signal control was not able to consistently yield significant lower average vehicle delay than a full actuated signal controller when evaluated on an intersection operating a coordinated timing plan. However, the research proposes the ability of an external agent to seamless control a traffic signal controller using real-time data, suggesting the encouraging results of this research can be improved upon