Resilient Output Feedback Control of Cyberphysical Systems

Cyber-physical system architectures are being used in many different applications such as power systems, transportation systems, process control systems, large-scale manufacturing systems, ecological systems, and health-care systems. Many of these applications involve safetycritical systems, and hence, any failures or cyber attacks can cause catastrophic damage to the physical system being controlled resulting in drastic societal ramifications. Due to the open communication and computation platform architectures of CPS, one of th most important challenges in these systems is their vulnerability to malicious cyber attacks. Cyber attacks can severely compromise system stability, performance, and integrity. In particular, malicious attacks in feedback control systems can compromise sensor measurements as well as actuator commands to severely degrade closed-loop system performance and integrity. Cyber attacks are continuously becoming more sophisticated and intelligent, and hence, it is vital to develop algorithms that can suppress their effects on cyber-physical systems.In this paper, an output feedback adaptive control architecture is presented to suppress or counteract the effect of false data injection actuator attacks in linear systems, where it is assumed that the attacker is capable of maliciously manipulating the controller commands to the actuators. In particular, the proposed controller is composed of two components, namely anominal controller and an additive corrective signal. It is assumed that the nominal controller has been already designed and implemented to achieve a desired closed-loop nominal performance. Using the nominal controller, an additive adaptive corrective signal is designed and added to the output of the nominal controller in order to suppress the effect of the actuator attacks. Thus, in the proposed control architecture, there is no need to redesign the nominal controller; only the adaptive corrective signal is designed using the available information from the nominal controller and the system.qscienc

Fault detection and isolation in a networked multi-vehicle unmanned system

Recent years have witnessed a strong interest and intensive research activities in the area of networks of autonomous unmanned vehicles such as spacecraft formation flight, unmanned aerial vehicles, autonomous underwater vehicles, automated highway systems and multiple mobile robots. The envisaged networked architecture can provide surpassing performance capabilities and enhanced reliability; however, it requires extending the traditional theories of control, estimation and Fault Detection and Isolation (FDI). One of the many challenges for these systems is development of autonomous cooperative control which can maintain the group behavior and mission performance in the presence of undesirable events such as failures in the vehicles. In order to achieve this goal, the team should have the capability to detect and isolate vehicles faults and reconfigure the cooperative control algorithms to compensate for them. This dissertation deals with the design and development of fault detection and isolation algorithms for a network of unmanned vehicles. Addressing this problem is the main step towards the design of autonomous fault tolerant cooperative control of network of unmanned systems. We first formulate the FDI problem by considering ideal communication channels among the vehicles and solve this problem corresponding to three different architectures, namely centralized, decentralized, and semi-decentralized. The necessary and sufficient solvability conditions for each architecture are also derived based on geometric FDI approach. The effects of large environmental disturbances are subsequently taken into account in the design of FDI algorithms and robust hybrid FDI schemes for both linear and nonlinear systems are developed. Our proposed robust FDI algorithms are applied to a network of unmanned vehicles as well as Almost-Lighter-Than-Air-Vehicle (ALTAV). The effects of communication channels on fault detection and isolation performance are then investigated. A packet erasure channel model is considered for incorporating stochastic packet dropout of communication channels. Combining vehicle dynamics and communication links yields a discrete-time Markovian Jump System (MJS) mathematical model representation. This motivates development of a geometric FDI framework for both discrete-time and continuous-time Markovian jump systems. Our proposed FDI algorithm is then applied to a formation flight of satellites and a Vertical Take-Off and Landing (VTOL) helicopter problem. Finally, we investigate the problem of fault detection and isolation for time-delay systems as well as linear impulsive systems. The main motivation behind considering these two problems is that our developed geometric framework for Markovian jump systems can readily be applied to other class of systems. Broad classes of time-delay systems, namely, retarded, neutral, distributed and stochastic time-delay systems are investigated in this dissertation and a robust FDI algorithm is developed for each class of these systems. Moreover, it is shown that our proposed FDI algorithms for retarded and stochastic time-delay systems can potentially be applied in an integrated design of FDI/controller for a network of unmanned vehicles. Necessary and sufficient conditions for solvability of the fundamental problem of residual generation for linear impulsive systems are derived to conclude this dissertation

A component map tuning method for performance prediction and diagnostics of gas turbine compressors

In this paper, a novel compressor map tuning method is developed with the primary objective of improving the accuracy and fidelity of gas turbine engine models for performance prediction and diagnostics. A new compressor map fitting and modeling method is introduced to simultaneously determine the best elliptical curves to a set of compressor map data. The coefficients that determine the shape of the compressor map curves are analyzed and tuned through a multi-objective optimization scheme in order to simultaneously match multiple sets of engine performance measurements. The component map tuning method, that is developed in the object oriented Matlab Simulink environment, is implemented in a dynamic gas turbine engine model and tested in off-design steady state and transient as well as degraded operating conditions. The results provided demonstrate and illustrate the capabilities of our proposed method in refining existing engine performance models to different modes of the gas turbine operation. In addition, the excellent agreement between the injected and the predicted degradation of the engine model demonstrates the potential of the proposed methodology for gas turbine diagnostics. The proposed method can be integrated with the performance-based tools for improved condition monitoring and diagnostics of gas turbine power plants. © 2014 Elsevier Ltd

SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank's control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin

Fault Tolerant Control of Multiple Mobile Robots

Recently, usage of autonomous wheeled mobile robots (WMRs) is significantly increased in different industries such as manufacturing, health care and military and there exist stringent requirements for their safe and reliable operation in industrial/commercial environments. In addition, autonomous multi-agent mobile robot systems in which specific numbers of robots are cooperating with each other to accomplish a task is becoming more demanding in different industries in the age of technology enhancement. Consequently, development of fault tolerant controller (FTC) for WMRs is a vital research problem to be addressed in order to enhance the safety and reliability of mobile robots. The main aim of this paper is to develop an actuator fault tolerant controller for both single and multiple-mobile robot applications with the main focus on differential derive mobile robots. Initially, a fault tolerant controller is developed for loss of effectiveness actuator faults in differential drive mobile robots while tracking a desired trajectory. The heading and position of the differential drive mobile robot is controlled through angular velocity of left and right wheels. The actuator loss of effectiveness fault is modeled on the kinematic equation of the robot as a multiplicative gain in the left and right wheels angular velocity. Accordingly, the aim is to estimate the described gains using joint parameter and state estimation framework. Toward this goal, the augmented discrete time nonlinear model of the robot is considered. Based on the extended Kalman filter technique, a joint parameter and state estimation method is used to estimate the actuator loss of effectiveness gains as the parameters of the system, as well as the states of the system. The estimated gains are then used in the controller to compensate the effect of actuator faults on the performance of mobile robots. In addition, the proposed FTC method is extended for the leader-follower formation control of mobile robots in the presence of fault in either leader or followers. Multi agent mobile robot system is designed to track a trajectory while keeping a desired formation in the presence of actuator loss of effectiveness faults. It is assumed that the leader controller is independent from the followers and is designed based on the FTC frame work developed earlier in this document. Also, the fault is modeled in the kinematic equation of the robot as a multiplicative gain and augmented discrete-time nonlinear model is used to estimate the loss of effectiveness gains. The follower controller is designed based on feedback linearization approach with respect to the coordinates of the leader robot. An extended Kalman filter is used for each robot to estimate parameters and states of the system and as the fault is detected in any of the followers, the corresponding controller compensates the fault. Finally, the efficacy of the proposed FTC framework for both single and multiple mobile robots is demonstrated by experimental results using Qbot-2 from Quanser. To sum up, a fault tolerant controller scheme is proposed for differential drive mobile robots in the presence of loss of effectiveness actuator faults. A joint parameter and state estimation scheme is utilized based on EKF approach to estimate parameters (actuator loss of effectiveness) and the system states. The effect of the estimated fault is compensated in the controller for both single robot and formation control of multiple mobile robots. The proposed schemes are experimentally validated on Qbot-2 robots.qscienc