Resilient Parameter-Invariant Control With Application to Vehicle Cruise Control

This work addresses the general problem of resilient control of unknown stochastic linear time-invariant (LTI) systems in the presence of sensor attacks. Motivated by a vehicle cruise control application, this work considers a first order system with multiple measurements, of which a bounded subset may be corrupted. A frequency-domain-designed resilient parameter-invariant controller is introduced that simultaneously minimizes the effect of corrupted sensors, while maintaining a desired closed-loop performance, invariant to unknown model parameters. Simulated results illustrate that the resilient parameter-invariant controller is capable of stabilizing unknown state disturbances and can perform state trajectory tracking

On Resilient Control for Secure Connected Vehicles: A Hybrid Systems Approach

According to the Internet of Things Forecast conducted by Ericsson, connected devices will be around 29 billion by 2022. This technological revolution enables the concept of Cyber-Physical Systems (CPSs) that will transform many applications, including power-grid, transportation, smart buildings, and manufacturing. Manufacturers and institutions are relying on technologies related to CPSs to improve the efficiency and performances of their products and services. However, the higher the number of connected devices, the higher the exposure to cybersecurity threats. In the case of CPSs, successful cyber-attacks can potentially hamper the economy and endanger human lives. Therefore, it is of paramount importance to develop and adopt resilient technologies that can complement the existing security tools to make CPSs more resilient to cyber-attacks. By exploiting the intrinsically present physical characteristics of CPSs, this dissertation employs dynamical and control systems theory to improve the CPS resiliency to cyber-attacks. In particular, we consider CPSs as Networked Control Systems (NCSs), which are control systems where plant and controller share sensing and actuating information through networks. This dissertation proposes novel design procedures that maximize the resiliency of NCSs to network imperfections (i.e., sampling, packet dropping, and network delays) and denial of service (DoS) attacks. We model CPSs from a general point of view to generate design procedures that have a vast spectrum of applicability while creating computationally affordable algorithms capable of real-time performances. Indeed, the findings of this research aspire to be easily applied to several CPSs applications, e.g., power grid, transportation systems, and remote surgery. However, this dissertation focuses on applying its theoretical outcomes to connected and automated vehicle (CAV) systems where vehicles are capable of sharing information via a wireless communication network. In the first part of the dissertation, we propose a set of LMI-based constructive Lyapunov-based tools for the analysis of the resiliency of NCSs, and we propose a design approach that maximizes the resiliency. In the second part of the thesis, we deal with the design of DOS-resilient control systems for connected vehicle applications. In particular, we focus on the Cooperative Adaptive Cruise Control (CACC), which is one of the most popular and promising applications involving CAVs

Compositional Set Invariance in Network Systems with Assume-Guarantee Contracts

This paper presents an assume-guarantee reasoning approach to the computation of robust invariant sets for network systems. Parameterized signal temporal logic (pSTL) is used to formally describe the behaviors of the subsystems, which we use as the template for the contract. We show that set invariance can be proved with a valid assume-guarantee contract by reasoning about individual subsystems. If a valid assume-guarantee contract with monotonic pSTL template is known, it can be further refined by value iteration. When such a contract is not known, an epigraph method is proposed to solve for a contract that is valid, ---an approach that has linear complexity for a sparse network. A microgrid example is used to demonstrate the proposed method. The simulation result shows that together with control barrier functions, the states of all the subsystems can be bounded inside the individual robust invariant sets.Comment: Submitted to 2019 American Control Conferenc

Security of Vehicular Platooning

Platooning concept involves a group of vehicles acting as a single unit through coordination of movements. While Platooning as an evolving trend in mobility and transportation diminishes the individual and manual driving concerns, it creates new risks. New technologies and passenger’s safety and security further complicate matters and make platooning attractive target for the malicious minds. To improve the security of the vehicular platooning, threats and their potential impacts on vehicular platooning should be identified to protect the system against security risks. Furthermore, algorithms should be proposed to detect intrusions and mitigate the effects in case of attack. This dissertation introduces a new vulnerability in vehicular platooning from the control systems perspective and presents the detection and mitigation algorithms to protect vehicles and passengers in the event of the attack

An Efficient Resilient MPC Scheme via Constraint Tightening against Cyberattacks: Application to Vehicle Cruise Control

We propose a novel framework for designing a resilient Model Predictive Control (MPC) targeting uncertain linear systems under cyber attack. Assuming a periodic attack scenario, we model the system under Denial of Service (DoS) attack, also with measurement noise, as an uncertain linear system with parametric and additive uncertainty. To detect anomalies, we employ a Kalman filter-based approach. Then, through our observations of the intensity of the launched attack, we determine a range of possible values for the system matrices, as well as establish bounds of the additive uncertainty for the equivalent uncertain system. Leveraging a recent constraint tightening robust MPC method, we present an optimization-based resilient algorithm. Accordingly, we compute the uncertainty bounds and corresponding constraints offline for various attack magnitudes. Then, this data can be used efficiently in the MPC computations online. We demonstrate the effectiveness of the developed framework on the Adaptive Cruise Control (ACC) problem.Comment: To Appear in ICINCO 202

Design and Implementation of Attack-Resilient Cyber-Physical Systems

Recent years have witnessed a significant increase in the number of security-related incidents in control systems. These include high-profile attacks in a wide range of application domains, from attacks on critical infrastructure, as in the case of the Maroochy Water breach [1], and industrial systems (such as the StuxNet virus attack on an industrial supervisory control and data acquisition system [2], [3] and the German Steel Mill cyberattack [4], [5]), to attacks on modern vehicles [6]-[8]. Even high-assurance military systems were shown to be vulnerable to attacks, as illustrated in the highly publicized downing of the RQ-170 Sentinel U.S. drone [9]-[11]. These incidents have greatly raised awareness of the need for security in cyberphysical systems (CPSs), which feature tight coupling of computation and communication substrates with sensing and actuation components. However, the complexity and heterogeneity of this next generation of safety-critical, networked, and embedded control systems have challenged the existing design methods in which security is usually consider as an afterthought

A Resilient Control Approach to Secure Cyber Physical Systems (CPS) with an Application on Connected Vehicles

The objective of this dissertation is to develop a resilient control approach to secure Cyber Physical Systems (CPS) against cyber-attacks, network failures and potential physical faults. Despite being potentially beneficial in several aspects, the connectivity in CPSs poses a set of specific challenges from safety and reliability standpoint. The first challenge arises from unreliable communication network which affects the control/management of overall system. Second, faulty sensors and actuators can degrade the performance of CPS and send wrong information to the controller or other subsystems of the CPS. Finally, CPSs are vulnerable to cyber-attacks which can potentially lead to dangerous scenarios by affecting the information transmitted among various components of CPSs. Hence, a resilient control approach is proposed to address these challenges. The control approach consists of three main parts:(1) Physical fault diagnostics: This part makes sure the CPS works normally while there is no cyber-attacks/ network failure in the communication network; (2) Cyber-attack/failure resilient strategy: This part consists of a resilient strategy for specific cyber-attacks to compensate for their malicious effects ; (3) Decision making algorithm: The decision making block identifies the specific existing cyber-attacks/ network failure in the system and deploys corresponding control strategy to minimize the effect of abnormality in the system performance. In this dissertation, we consider a platoon of connected vehicle system under Co-operative Adaptive Cruise Control (CACC) strategy as a CPS and develop a resilient control approach to address the aforementioned challenges. The first part of this dissertation investigates fault diagnostics of connected vehicles assuming ideal communication network. Very few works address the real-time diagnostics problem in connected vehicles. This study models the effect of different faults in sensors and actuators, and also develops fault diagnosis scheme for detectable and identifiable faults. The proposed diagnostics scheme is based on sliding model observers to detect, isolate and estimate faults in the sensors and actuators. One of the main advantages of sliding model approach lies in applicability to nonlinear systems. Therefore, the proposed method can be extended for other nonlinear cyber physical systems as well. The second part of the proposed research deals with developing strategies to maintain performance of cyber-physical systems close to the normal, in the presence of common cyber-attacks and network failures. Specifically, the behavior of Dedicated Short-Range Communication (DSRC) network is analyzed under cyber-attacks and failures including packet dropping, Denial of Service (DOS) attack and false data injection attack. To start with, packet dropping in network communication is modeled by Bernoulli random variable. Then an observer based modifying algorithm is proposed to modify the existing CACC strategy against the effect of packet dropping phenomena. In contrast to the existing works on state estimation over imperfect communication network in CPS which mainly use either holding previous received data or Kalman filter with intermittent observation, a combination of these two approaches is used to construct the missing data over packet dropping phenomena. Furthermore, an observer based fault diagnostics based on sliding mode approach is proposed to detect, isolate and estimate sensor faults in connected vehicles platoon. Next, Denial of Service (DoS) attack is considered on the communication network. The effect of DoS attack is modeled as an unknown stochastic delay in data delivery in the communication network. Then an observer based approach is proposed to estimate the real data from the delayed measured data over the network. A novel approach based on LMI theory is presented to design observer and estimate the states of the system via delayed measurements. Next, we explore and alternative approach by modeling DoS with unknown constant time delay and propose an adaptive observer to estimate the delay. Furthermore, we study the effects of system uncertainties on the DoS algorithm. In the third algorithm, we considered a general CPS with a saturated DoS attack modeled with constant unknown delay. In this part, we modeled the DoS via a PDE and developed a PDE based observer to estimate the delay as well as states of the system while the only available measurements are delayed. Furthermore, as the last cyber-attack of the second part of the dissertation, we consider false data injection attack as the fake vehicle identity in the platoon of vehicles. In this part, we develop a novel PDE-based modeling strategy for the platoon of vehicles equipped with CACC. Moreover, we propose a PDE based observer to detect and isolate the location of the false data injection attack injected into the platoon as fake identity. Finally, the third part of the dissertation deals with the ongoing works on an optimum decision making strategy formulated via Model Predictive Control (MPC). The decision making block is developed to choose the optimum strategy among available strategies designed in the second part of the dissertation