A secure state estimation algorithm for nonlinear systems under sensor attacks

The state estimation of continuous-time nonlinear systems in which a subset of sensor outputs can be maliciously controlled through injecting a potentially unbounded additive signal is considered in this paper. Analogous to our earlier work for continuous-time linear systems in \cite{chong2015observability}, we term the convergence of the estimates to the true states in the presence of sensor attacks as `observability under $M$ attacks', where $M$ refers to the number of sensors which the attacker has access to. Unlike the linear case, we only provide a sufficient condition such that a nonlinear system is observable under $M$ attacks. The condition requires the existence of asymptotic observers which are robust with respect to the attack signals in an input-to-state stable sense. We show that an algorithm to choose a compatible state estimate from the state estimates generated by the bank of observers achieves asymptotic state reconstruction. We also provide a constructive method for a class of nonlinear systems to design state observers which have the desirable robustness property. The relevance of this study is illustrated on monitoring the safe operation of a power distribution network.Comment: This paper has been accepted for publication at the 59th IEEE Conference on Decision and Control, 202

Images and depth for high resolution, low-latency sensing and security applications

The thesis focuses on using images and depths for high resolution, low latency sensing, and then using these sensing techniques to build security applications. First, we introduce the usefulness of high quality depth sensing, and the difficulty to acquire such depth stream via pure hardware approach. Then, we propose our sensor fusion approach, which combines depth camera and color camera. Chapter 2 puts forward a low cost approach to use a high spatial resolution color stream to help aggressively increase the spatial resolution of the depth stream. Continuing this direction, Chapter 3 proposes to use optical ow to forward warp the depth stream according to a high frequency, low latency CMOS color stream. The warping can create a high frequency, low latency depth stream. In both Chapter 2 and Chapter 3, we show that the improved depth sensing can benefit lots of applications. In Chapter 4, we propose a SafetyNet, which can reliably detecting and rejecting adversarial examples. With the revolutionary SafetyNet architecture and the advanced depth sensing, we can reliably prove to users whether a picture of a scene is real or not. In sum, the thesis focuses on improving sensing technologies and building vision and security applications around the sensing technologies

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

Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm: Part Ⅱ, Optimization in flight mission and controller gains correlation development

This paper proposes a Linkage Learning Genetic Algorithm (LLGA) based on the messy Genetic Algorithm (mGA) to optimize the Min-Max fuel controller performance in Gas Turbine Engine (GTE). For this purpose, a GTE fuel controller Simulink model based on the Min-Max selection strategy is firstly built. Then, the objective function that considers both performance indices (response time and fuel consumption) and penalty items (fluctuation, tracking error, overspeed and acceleration/deceleration) is established to quantify the controller performance. Next, the task to optimize the fuel controller is converted to find the optimization gains combination that could minimize the objective function while satisfying constraints and limitations. In order to reduce the optimization time and to avoid trapping in the local optimums, two kinds of building block detection methods including lower fitness value method and bigger fitness value change method are proposed to determine the most important bits which have more contribution on fitness value of the chromosomes. Then the procedures to apply LLGA in controller gains tuning are specified stepwise and the optimization results in runway condition are depicted subsequently. Finally, the comparison is made between the LLGA and the simple GA in GTE controller optimization to confirm the effectiveness of the proposed approach. The results show that the LLGA method can get better solution than simple GA within the same iterations or optimization time. The extension applications of the LLGA method in other flight conditions and the complete flight mission simulation will be carried out in part I

Trustworthiness in Mobile Cyber Physical Systems

Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the ‘cyberworld’ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS