A New Switched State Jump Observer for Traffic Density Estimation in Expressways Based on Hybrid-Dynamic-Traffic-Network-Model

When faced with problems such as traffic state estimation, state prediction, and congestion identification for the expressway network, a novel switched observer design strategy with jump states is required to reconstruct the traffic scene more realistically. In this study, the expressway network is firstly modeled as the special discrete switched system, which is called the piecewise affine system model, a partition of state subspace is introduced, and the convex polytopes are utilized to describe the combination modes of cells. Secondly, based on the hybrid dynamic traffic network model, the corresponding switched observer (including state jumps) is designed. Furthermore, by applying multiple Lyapunov functions and S-procedure theory, the observer design problem can be converted into the existence issue of the solutions to the linear matrix inequality. As a result, a set of gain matrices can be obtained. The estimated states start to jump when the mode changes occur, and the updated value of the estimated state mainly depends on the estimated and the measured values at the previous time. Lastly, the designed state jump observer is applied to the Beijing Jingkai expressway, and the superiority and the feasibility are demonstrated in the application results

Benelux meeting on systems and control, 23rd, March 17-19, 2004, Helvoirt, The Netherlands

Book of abstract

Autonomous vehicles: From vehicular control to traffic control

International audienc

Multi-Sensor Data Fusion for Travel Time Estimation

The importance of travel time estimation has increased due to the central role it plays in a number of emerging intelligent transport systems and services including Advanced Traveller Information Systems (ATIS), Urban Traffic Control (UTC), Dynamic Route Guidance (DRG), Active Traffic Management (ATM), and network performance monitoring. Along with the emerging of new sensor technologies, the much greater volumes of near real time data provided by these new sensor systems create opportunities for significant improvement in travel time estimation. Data fusion as a recent technique leads to a promising solution to this problem. This thesis presents the development and testing of new methods of multi-sensor data fusion for the accurate, reliable and robust estimation of travel time. This thesis reviews the state-of-art data fusion approaches and its application in transport domain, and discusses both of opportunities and challenging of applying data fusion into travel time estimation in a heterogeneous real time data environment. For a particular England highway scenario where ILDs and ANPR data are largely available, a simple but practical fusion method is proposed to estimate the travel time based on a novel relationship between space-mean-speed and time-mean-speed. In developing a general fusion framework which is able to fuse ILDs, GPS and ANPR data, the Kalman filter is identified as the most appropriate fundamental fusion technique upon which to construct the required framework. This is based both on the ability of the Kalman filter to flexibly accommodate well-established traffic flow models which describe the internal physical relation between the observed variables and objective estimates and on its ability to integrate and propagate in a consistent fashion the uncertainty associated with different data sources. Although the standard linear Kalman filter has been used for multi-sensor travel time estimation in the previous research, the novelty of this research is to develop a nonlinear Kalman filter (EKF and UKF) fusion framework which improves the estimation performance over those methods based on the linear Kalman filter. This proposed framework is validated by both of simulation and real-world scenarios, and is demonstrated the effectiveness of estimating travel time by fusing multi-sensor sources

Systems and control : 21th Benelux meeting, 2002, March 19-21, Veldhoven, The Netherlands

Book of abstract

Cyber physical complex networks, modeling, analysis, and control

This research scrutinize various attributes of complex networks; mainly, modeling, sensing, estimation, safety analysis, and control. In this study, formal languages and finite automata are used for modeling incident management processes. Safety properties are checked in order to verify the system. This method introduces a systematic approach to incident management protocols that are governed by mostly unsystematic algorithms. A portion of the used data in this study is collected by means of radar and loop detectors. A weighted t-statistics methodology is developed in order to validate these detectors. The detector data is then used to extract travel time information where travel time reliability is investigated. Classical reliability measures are examined and compared with the new entropy based reliability measure proposed in this study. The novel entropy based reliability measure introduces a more consistent measure with the classical definition of travel time reliability than traditional measures. Furthermore, it measures uncertainty directly using the full distribution of the examined random variable where previously developed reliability measures only use first and second moments. Various approaches of measuring network reliability are also investigated in this study. Finally, feedback linearization control scheme is developed for a ramp meter that is modeled using Godunov\u27s conditions at the boundaries representing a switched system. This study demonstrates the advantages of implementing a feedback liberalized control scheme with recursive real time parameter estimation over the commonly practiced velocity based thresholds

Developments in Estimation and Control for Cloud-Enabled Automotive Vehicles.

Cloud computing is revolutionizing access to distributed information and computing resources that can facilitate future data and computation intensive vehicular control functions and improve vehicle driving comfort and safety. This dissertation investigates several potential Vehicle-to-Cloud-to-Vehicle (V2C2V) applications that can enhance vehicle control and enable additional functionalities by integrating onboard and cloud resources. Firstly, this thesis demonstrates that onboard vehicle sensors can be used to sense road profiles and detect anomalies. This information can be shared with other vehicles and transportation authorities within a V2C2V framework. The response of hitting a pothole is characterized by a multi-phase dynamic model which is validated by comparing simulation results with a higher-fidelity commercial modeling package. A novel framework of simultaneous road profile estimation and anomaly detection is developed by combining a jump diffusion process (JDP)-based estimator and a multi-input observer. The performance of this scheme is evaluated in an experimental vehicle. In addition, a new clustering algorithm is developed to compress anomaly information by processing anomaly report streams. Secondly, a cloud-aided semi-active suspension control problem is studied demonstrating for the first time that road profile information and noise statistics from the cloud can be used to enhance suspension control. The problem of selecting an optimal damping mode from a finite set of damping modes is considered and the best mode is selected based on performance prediction on the cloud. Finally, a cloud-aided multi-metric route planner is investigated in which safety and comfort metrics augment traditional planning metrics such as time, distance, and fuel economy. The safety metric is developed by processing a comprehensive road and crash database while the comfort metric integrates road roughness and anomalies. These metrics and a planning algorithm can be implemented on the cloud to realize the multi-metric route planning. Real-world case studies are presented. The main contribution of this part of the dissertation is in demonstrating the feasibility and benefits of enhancing the existing route planning algorithms with safety and comfort metrics.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120710/1/zhaojli_1.pd

Online Optimization of LTI Systems Under Persistent Attacks: Stability, Tracking, and Robustness

We study the stability properties of the interconnection of an LTI dynamical plant and a feedback controller that generates control signals that are compromised by a malicious attacker. We consider two classes of controllers: a static output-feedback controller, and a dynamical gradient-flow controller that seeks to steer the output of the plant towards the solution of a convex optimization problem. We analyze the stability of the closed-loop system under a class of switching attacks that persistently modify the control inputs generated by the controllers. The stability analysis leverages the framework of hybrid dynamical systems, Lyapunov-based arguments for switching systems with unstable modes, and singular perturbation theory. Our results reveal that under a suitable time-scale separation, the stability of the interconnected system can be preserved when the attack occurs with "sufficiently low frequency" in any bounded time interval. We present simulation results in a power-grid example that corroborate the technical findings

Security of Cyber-Physical Systems

Cyber-physical system (CPS) innovations, in conjunction with their sibling computational and technological advancements, have positively impacted our society, leading to the establishment of new horizons of service excellence in a variety of applicational fields. With the rapid increase in the application of CPSs in safety-critical infrastructures, their safety and security are the top priorities of next-generation designs. The extent of potential consequences of CPS insecurity is large enough to ensure that CPS security is one of the core elements of the CPS research agenda. Faults, failures, and cyber-physical attacks lead to variations in the dynamics of CPSs and cause the instability and malfunction of normal operations. This reprint discusses the existing vulnerabilities and focuses on detection, prevention, and compensation techniques to improve the security of safety-critical systems

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above