Learning-based Predictive Control Approach for Real-time Management of Cyber-physical Systems

Cyber-physical systems (CPSs) are composed of heterogeneous, and networked hardware and software components tightly integrated with physical elements [72]. Large-scale CPSs are composed of complex components, subject to uncertainties [89], as though their design and development is a challenging task. Achieving reliability and real-time adaptation to changing environments are some of the challenges involved in large-scale CPSs development [51]. Addressing these challenges requires deep insights into control theory and machine learning. This research presents a learning-based control approach for CPSs management, considering their requirements, specifications, and constraints. Model-based control approaches, such as model predictive control (MPC), are proven to be efficient in the management of CPSs [26]. MPC is a control technique that uses a prediction model to estimate future dynamics of the system and generate an optimal control sequence over a prediction horizon. The main benefit of MPC in CPSs management comes from its ability to take the predictions of system’s environmental conditions and disturbances into account [26]. In this dissertation, centralized and distributed MPC strategies are designed for the management of CPSs. They are implemented for the thermal management of a CPS case study, smart building. The control goals are optimizing system efficiency (lower thermal power consumption in the building), and improving users’ convenience (maintaining desired indoor thermal conditions in the building). Model-based control strategies are advantageous in the management of CPSs due to their ability to provide system robustness and stability. The performance of a model-based controller strongly depends on the accuracy of the model as a representation of the system dynamics [26]. Accurate modeling of large-scale CPSs is difficult (due to the existence of unmodeled dynamics and uncertainties in the modeling process); therefore, modelbased control approach is not practical for these systems [6]. By incorporating machine learning with model-based control strategies, we can address CPS modeling challenges while preserving the advantages of model-based control methods. In this dissertation, a learning-based modeling strategy incorporated with a model-based control approach is proposed to manage energy usage and maintain thermal, visual, and olfactory performance in buildings. Neural networks (NNs) are used to learn the building’s performance criteria, occupant-related parameters, environmental conditions, and operation costs. Control inputs are generated through the model-based predictive controller and based on the learned parameters, to achieve the desired performance. In contrast to the existing building control systems presented in the literature, the proposed management system integrates current and future information of occupants (convenience, comfort, activities), building energy trends, and environment conditions (environmental temperature, humidity, and light) into the control design. This data is synthesized and evaluated in each instance of decision-making process for managing building subsystems. Thus, the controller can learn complex dynamics and adapt to the changing environment, to achieve optimal performance while satisfying problem constraints. Furthermore, while many prior studies in the filed are focused on optimizing a single aspect of buildings (such as thermal management), and little attention is given to the simultaneous management of all building objectives, our proposed management system is developed considering all buildings’ physical models, environmental conditions, comfort specifications, and occupants’ preferences, and can be applied to various building management applications. The proposed control strategy is implemented to manage indoor conditions and energy consumption in a building, simulated in EnergyPlus software. In addition, for comparison purposes, we designed and simulated a baseline controller for the building under the same conditions

Thermal comfort control in smart home environment based on Cyber- Physical System

CPS is an integration of physical dynamics and computational systems, so they commonly combine both discrete and continuous dynamics. Therefore, modelling of cyber and physical processes is also known as hybrid system modelling.CPS-based hybrid temperature control system (HTC) in smart home is concerned with sensing and timely control the multiple actuators to maintain a desired temperature with the optimized cost through the communication network. The indoor temperature fluctuated by internal and external heat loads are always sensed by the sensors. With the closed-loop interactions, the indoor temperature is feedback to the controller. Then, the controller with the decision-making algorithm decides the control actions which is send to actuators or device controller via the communication network to change the state of the physical world and to satisfy the specified goal. In this project, we will be designing and implementing a thermal comfort control CPS by using MATLAB/Simulink. We will then optimize it as the CPS is built with multi actuators to achieve the lowest energy consumption at any given condition while maintain the building in the desired temperature

Protecting Actuators in Safety-Critical IoT Systems from Control Spoofing Attacks

In this paper, we propose a framework called Contego-TEE to secure Internet-of-Things (IoT) edge devices with timing requirements from control spoofing attacks where an adversary sends malicious control signals to the actuators. We use a trusted computing base available in commodity processors (such as ARM TrustZone) and propose an invariant checking mechanism to ensure the security and safety of the physical system. A working prototype of Contego-TEE was developed using embedded Linux kernel. We demonstrate the feasibility of our approach for a robotic vehicle running on an ARM-based platform.Comment: 2nd Workshop on the Internet of Things Security and Privacy - Iot S&P'19, November 15, 2019, London, United Kingdom. ACM ISBN: 978-1-4503-6838-4/19/1

Qduino: a cyber-physical programming platform for multicore Systems-on-Chip

Emerging multicore Systems-on-Chip are enabling new cyber-physical applications such as autonomous drones, driverless cars and smart manufacturing using web-connected 3D printers. Common to those applications is a communicating task pipeline, to acquire and process sensor data and produce outputs that control actuators. As a result, these applications usually have timing requirements for both individual tasks and task pipelines formed for sensor data processing and actuation. Current cyber-physical programming platforms, such as Arduino and embedded Linux with the POSIX interface do not allow application developers to specify those timing requirements. Moreover, none of them provide the programming interface to schedule tasks and map them to processor cores, while managing I/O in a predictable manner, on multicore hardware platforms. Hence, this thesis presents the Qduino programming platform. Qduino adopts the simplicity of the Arduino API, with additional support for real-time multithreaded sketches on multicore architectures. Qduino allows application developers to specify timing properties of individual tasks as well as task pipelines at the design stage. To this end, we propose a mathematical framework to derive each task’s budget and period from the specified end-to-end timing requirements. The second part of the thesis is motivated by the observation that at the center of these pipelines are tasks that typically require complex software support, such as sensor data fusion or image processing algorithms. These features are usually developed by many man-year engineering efforts and thus commonly seen on General-Purpose Operating Systems (GPOS). Therefore, in order to support modern, intelligent cyber-physical applications, we enhance the Qduino platform’s extensibility by taking advantage of the Quest-V virtualized partitioning kernel. The platform’s usability is demonstrated by building a novel web-connected 3D printer and a prototypical autonomous drone framework in Qduino

Detecting Safety and Security Faults in PLC Systems with Data Provenance

Programmable Logic Controllers are an integral component for managing many different industrial processes (e.g., smart building management, power generation, water and wastewater management, and traffic control systems), and manufacturing and control industries (e.g., oil and natural gas, chemical, pharmaceutical, pulp and paper, food and beverage, automotive, and aerospace). Despite being used widely in many critical infrastructures, PLCs use protocols which make these control systems vulnerable to many common attacks, including man-in-the-middle attacks, denial of service attacks, and memory corruption attacks (e.g., array, stack, and heap overflows, integer overflows, and pointer corruption). In this paper, we propose PLC-PROV, a system for tracking the inputs and outputs of the control system to detect violations in the safety and security policies of the system. We consider a smart building as an example of a PLC-based system and show how PLC-PROV can be applied to ensure that the inputs and outputs are consistent with the intended safety and security policies

Cyber Physical System for Continuous Evaluation of Fall Risks to Enable Aging-In-Place

Every year, one out of three adults over the age of 65 falls, and about 30% of the falls result in moderate to severe injuries. The high rate of fall-related hospitalizations and the fact that falls are a major source of morbidity and mortality in older adults have motivated extensive interdisciplinary clinical and engineering research with a focus on fall prevention. This research is aimed at developing a medical Cyber Physical System (CPS) composed of a human supervised mobile robot and ambient intelligence sensors to provide continuous evaluation of environmental risks in the home. As a preventive measure to avoid falls, we propose use of mobile robots to detect possible fall risks inside a house. As a step-up to that, we also define a control framework for intelligent, networked mobile robots to semi-autonomously perform assistive and preventive tasks. This framework is integrated in a smart home that provides monitoring and control capabilities of environmental conditions such as objects blocking pathways or uneven surfaces. The main outcome of this work is the realization of this system at Worcester Polytechnic Institute\u27s (WPI) @Home testbed

Cyberthreats, Attacks and Intrusion Detection in Supervisory Control and Data Acquisition Networks

Supervisory Control and Data Acquisition (SCADA) systems are computer-based process control systems that interconnect and monitor remote physical processes. There have been many real world documented incidents and cyber-attacks affecting SCADA systems, which clearly illustrate critical infrastructure vulnerabilities. These reported incidents demonstrate that cyber-attacks against SCADA systems might produce a variety of financial damage and harmful events to humans and their environment. This dissertation documents four contributions towards increased security for SCADA systems. First, a set of cyber-attacks was developed. Second, each attack was executed against two fully functional SCADA systems in a laboratory environment; a gas pipeline and a water storage tank. Third, signature based intrusion detection system rules were developed and tested which can be used to generate alerts when the aforementioned attacks are executed against a SCADA system. Fourth, a set of features was developed for a decision tree based anomaly based intrusion detection system. The features were tested using the datasets developed for this work. This dissertation documents cyber-attacks on both serial based and Ethernet based SCADA networks. Four categories of attacks against SCADA systems are discussed: reconnaissance, malicious response injection, malicious command injection and denial of service. In order to evaluate performance of data mining and machine learning algorithms for intrusion detection systems in SCADA systems, a network dataset to be used for benchmarking intrusion detection systemswas generated. This network dataset includes different classes of attacks that simulate different attack scenarios on process control systems. This dissertation describes four SCADA network intrusion detection datasets; a full and abbreviated dataset for both the gas pipeline and water storage tank systems. Each feature in the dataset is captured from network flow records. This dataset groups two different categories of features that can be used as input to an intrusion detection system. First, network traffic features describe the communication patterns in a SCADA system. This research developed both signature based IDS and anomaly based IDS for the gas pipeline and water storage tank serial based SCADA systems. The performance of both types of IDS were evaluates by measuring detection rate and the prevalence of false positives

Distributed Control for Cyber-Physical Systems

Networked Cyber-Physical Systems (CPS) are fundamentally constrained by the tight coupling and closed-loop control and actuation of physical processes. To address actuation in such closed-loop wireless control systems there is a strong need to re-think the communication architectures and protocols for maintaining stability and performance in the presence of disturbances to the network, environment and overall system objectives. We review the current state of network control efforts for CPS and present two complementary approaches for robust, optimal and composable control over networks. We first introduce a computer systems approach with Embedded Virtual Machines (EVM), a programming abstraction where controller tasks, with their control and timing properties, are maintained across physical node boundaries. Controller functionality is decoupled from the physical substrate and is capable of runtime migration to the most competent set of physical controllers to maintain stability in the presence of changes to nodes, links and network topology. We then view the problem from a control theoretic perspective to deliver fully distributed control over networks with Wireless Control Networks (WCN). As opposed to traditional networked control schemes where the nodes simply route information to and from a dedicated controller, our approach treats the network itself as the controller. In other words, the computation of the control law is done in a fully distributed way inside the network. In this approach, at each time-step, each node updates its internal state to be a linear combination of the states of the nodes in its neighborhood. This causes the entire network to behave as a linear dynamical system, with sparsity constraints imposed by the network topology. This eliminates the need for routing between “sensor → channel → dedicated controller/estimator → channel → actuator”, allows for simple transmission scheduling, is operational on resource constrained low-power nodes and allows for composition of additional control loops and plants. We demonstrate the potential of such distributed controllers to be robust to a high degree of link failures and to maintain stability even in cases of node failures