Control Of Cyber-physical Systems Research Articles

Security switching control of cyber-physical system with incremental quadratic constraints under denial-of-service attack

This paper presents an observer-based security control scheme for a nonlinear cyber-physical system (CPS). Specially, the nonlinearity satisfies the incremental quadratic constraints ([Formula: see text]) which can represent many types of common nonlinearities. The problems are discussed under the assumption that both the feedback and forward channels may suffer from denial-of-service (DoS) attacks. The attack model is constructed in the form of time-delayed switching system with four subsystems corresponding to different attack situations. Then by using the attack model, an observer-based controller is constructed in the cyber layer to stabilize the CPS encountering DoS attacks. The closed-loop stability is analyzed based on H∞ index of switching systems in view of average dwell time (ADT) concept. Besides, the gain matrices of the controller and the observer are obtained by solving a series of matrix inequalities using an algorithm designed in this paper. Finally, the performance of security control scheme is presented by a simulation example.

Event-triggered adaptive sliding mode control of cyber-physical systems under false data injection attack

This paper studies the event-triggered adaptive sliding mode control(ASMC) problem for the cyber-physical systems (CPSs) under the false data injection attack (FDIA). First, for the unknown upper bound information of the false data injection attack, the adaptive technique is used to estimate the upper bound of the attack mode. Then the event-triggered ASMC method is developed by combining with the event-triggered mechanism. Second, Lyapunov’s stability theory is used to prove the admissibility of the formed event-triggered ASMC design scheme. Finally, simulation results verifies the effectiveness and superiority of the proposed method, which ensures the security and stability of the system and improve the utilization of information resource.

Anti-saturation resilient control of cyber-physical systems under actuator attacks

The security of cyber-physical systems (CPSs) against cyberattacks is essential in critical applications. Resilient control is an effective CPS security method that aims to mitigate cyberattacks in the physical domain of the CPSs. While resilient control has been investigated for different types of attacks, actuator saturation caused by cyberattacks has not been mentioned and addressed. To tackle this issue, a resilient control strategy is presented in this paper for CPSs under actuator saturation resulting from cyberattacks. It consists of an extended state observer and an anti-saturation resilient controller. The state observer estimates unknown system states and attacks, while the controller aims to resist the actuator saturation. The controller is designed by adopting Barrier Lyapunov function, Hyperbolic tangent sigmoid function, and the Nussbaum function. The CPSs with the resilient control strategy presented in this paper not only maintain their stability but also behave with enhanced resilience against cyberattacks, Case studies on a cyber-physical robotic arm system are conducted to demonstrate the effectiveness of the designed resilient control strategy.

Adaptive finite-time control for cyber-physical systems with injection and deception attacks

This paper discusses the problem of adaptive finite-time control of cyber-physical systems subject to injection and deception attacks (IDAs). The distinctive feature of the system is the coexistence of IDAs in the cyber layer and unknown parameters in the physical layer. Cyber attacks are modeled as state-dependent functions, which may change the direction of the state variables. To overcome the aforementioned issues, an adaptive finite-time controller is built using adding a power integrator technique and Nussbaum-function method, which may reduce the demands of the attack information. It is shown that the system states can converge to a neighbourhood of the origin in finite-time. Two numerical examples in this paper verified the scheme are effective.

Attack-Resilient Control Against FDI Attacks in Cyber-Physical Systems

Dear editor, This letter is concerned with the control of cyber-physical systems (CPSs) in the presence of malicious false data injection (FDI) attacks on actuators. The FDI attacks on actuators may result in faults of actuators or even the instability of CPSs. To tackle this problem, an unknown input observer (UIO) is proposed to estimate the system states and attack signals. For the aim of suppressing the impact of FDI attacks, a discrete-time sliding mode control (DSMC) algorithm is correspondingly put forward, where its reaching law is constructed based on the <tex>$n$</tex>-th order difference of the estimation of attack signals. Finally, two simulation instances are presented to show the effectiveness of the proposed method.

Dynamic event-triggered security control of cyber-physical systems against missing measurements and cyber-attacks

The security control approach is presented for cyber-physical systems (CPSs) with missing measurements and cyber-attacks based on an improved dynamic event-triggered scheme (DETS). The DETS is proposed to decrease the communication workload and reduce the effects of mutation data which may be erroneous. The sensor measurements are assumed to be lost randomly due to the unreliable network. In this paper, an observer-based controller is derived which can be tolerant towards the impacts of the missing measurements and cyber-attacks. The observer-based controller parameters and event-triggered parameter are co-designed. Finally, simulation results verify the validity of the proposed approach.

Adaptive Resilient Control of Cyber-Physical Systems Under Actuator and Sensor Attacks

Resilient control of cyber-physical systems (CPSs) against actuator and/or sensor attacks has been extensively researched. However, the existing research considers actuator attacks and sensor attacks separately and also designs resilient controllers based on complex nonlinear system models caused by unknown actuator and sensor attacks. This increases the difficulty in the analysis, computation, and control of CPSs under attacks. To address this issue, this article introduces an idea to deal with both actuator attacks and sensor attacks together with feedback linearization control. This simplifies the mathematical modeling of attacked CPSs, thus reducing the difficulty of resilient controller design. Then, from the simplified modeling, a composite controller is designed to enhance system resilience. It ensures the dynamic and steady-state performance of CPSs under attacks. Simulation studies are undertaken to demonstrate the effectiveness of the proposed method.

Resilient observer-based event-triggered control for cyber-physical systems under asynchronous denial-of-service attacks

Resilient observer-based event-triggered control for cyber-physical systems under asynchronous denial-of-service attacks

Real-time digital twin-based optimization with predictive simulation learning

ABSTRACT Digital twinning presents an exciting opportunity enabling real-time optimization of the control and operations of cyber-physical systems (CPS) with data-driven simulations, while facing prohibitive computational burdens. This paper introduces a method, Sequential Allocation using Machine-learning Predictions as Light-weight Estimates (SAMPLE) to address this computational challenge by leveraging machine learning models trained off-line in a predictive simulation learning setting prior to a real-time decision. SAMPLE integrates machine learning predictions with data generated by real-time execution of a digital twin in a rigorous yet flexible way, and optimally guides the digital twin simulation to achieve the computational efficiency required for real-time decision-making in a CPS. Numerical experiments demonstrate the viability of SAMPLE to select optimal decisions in real-time for CPS control and operations, compared to those of using only machine learning or simulations.

Mechanisms for Precise Virtual Time Advancement in Network Emulation

Network emulators enable rapid prototyping and testing of applications. In a typical emulation, the execution order and process execution burst lengths are managed by the host platform’s operating system, largely independent of the emulator. Timerbased mechanisms are typically used, but the imprecision of timer firings introduces imprecision in the advancement of time. This leads to statistical variation in behavior that is not due to the model. This article describes an open-source tool called Kronos, which provides a set of mechanisms for precise instruction-level tracking of process execution and control over execution order of containers, thus improving the mapping of executed behavior to advancement in time. This, and control of execution and placement of emulated processes in virtual time make the behavior of the emulation independent of the CPU resources of the platform that hosts the emulation. Under Kronos each process has its own virtual clock that is advanced based on a count of the number of \( \times \) 86 assembly instructions executed by its children. Two types of instruction counting techniques are discussed: (1) hardware-assisted mechanisms that are transparent to the executing application and (2) binary instrumentation-assisted mechanisms that modify the executing binary. We analyze the overheads associated with each approach and experimentally demonstrate the impact of Kronos’ time advancement precision by comparing it against emulations that, like Kronos, are embedded in virtual time, but unlike Kronos rely on Linux timers to control virtual machines and measure their progress in virtual time. We present two useful applications where Kronos aids in generating high-fidelity emulation results at low hardware costs: (1) analyzing protocol performance and (2) enabling analysis of cyber physical control systems. We also discuss limitations associated with simple linear conversions between instruction counts and ascribed virtual time and develop and evaluate more accurate virtual time conversion models.

$L_2$ Gain Secure Control of Cyber-Physical Systems Under Fast Time-Varying Cyber Attacks

Cyber attacks may cause significant damage to cyber-physical systems (CPSs). Secure control is an effective measure to resist cyber attacks. While efforts have been made in developing secure control methods, the secure control problem for fast time-varying cyber attacks has not been well investigated. This paper presents a secure control strategy for mitigating fast time-varying actuator attacks in CPSs. For attacked CPSs, a nonlinear disturbance observer (NDO) is firstly constructed to estimate the lumped uncertainty. Then, to ensure the security of attacked CPSs, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula> gain secure controller is designed to guarantee the stability of the CPSs. It is capable of handling not only constant and slow time-varying attacks but also fast time-varying cyber attacks to a certain extent. Compared with existing secure controllers, the secure controller designed in this paper withstands attacks with a wider range of frequencies, thus enhancing the resilience of the CPSs against unknown attacks. Simulations are conducted to verify the effectiveness of the proposed secure control strategy.

Design of Heterogeneous Cyber-Physical Systems Employing Category Theory

Heterogeneous cyber-physical control systems based on digital twins are in demand by Industry 4.0. In accordance with the contemporary systems engineering methodology, such systems are designed at the level of digital models. The paper proposes approaches to formalization and subsequent automation of solving direct and inverse problems of their design. To unify descriptions of heterogeneous components, we follow a viewpoint-based approach to architecture design recommended by the international standard ISO/IEC/IEEE 42010. Following recent trends, we employ category theory as a mathematical framework for the formal description and solution of design problems. Indeed, category theory is a branch of higher algebra specifically aimed at a unified representation of objects of different nature and relationships between them. The design space of a heterogeneous cyber-physical system is constructed as a subcategory of the multicomma category, the objects of which describe possible system architectures with a fixed structural hierarchy represented from a certain viewpoint as diagrams, and morphisms denote actions associated with the parts selection and replacement during the system design. Direct design problems consist in evaluating the properties of the system as a whole by its architecture and are solved using a universal category-theoretic construction of the colimit of the diagram. The solution of inverse problems that require finding variants of the system architecture, which are (sub-, Pareto-) optimal according to the consumer quality criteria, consists in reconstructing diagrams by their colimit edges. For such reconstruction, optimization algorithms of gradient descent type are reasonable to employ, which navigate along the system design space morphisms calculating the path by means of computer algebra. Typical techniques of assembling cyber-physical systems, such as modular composition and aspect weaving, are described in the language of category theory and illustrated. As an example, we outline the design of energy-efficient robotic production lines represented from the behavior viewpoint as discrete-event simulation models.

Research on cyber-physical system control strategy under false data injection attack perception

This paper investigates the security control problem of the cyber-physical system under false data injection attacks. A model predictive switching control strategy based on attack perception is proposed to compensate for the untrusted sequence of data caused by false data injection attacks. First, the binary attack detector is applied whether the system has suffered the attack. If the attack occurs, multistep correction is carried out for the future data according to the previous time data, and the waiting period [Formula: see text] is set. The input and output sequence of the controller is reconstructed, and the system is modeled as a constant time-delay switched system. Subsequently, the Lyapunov methods and average-dwell time are combined to provide sufficient conditions for the asymptotical stability of closed-loop switched system. Finally, the simulation of the networked first-order inverted pendulum model reveals that the control technique can efficiently suppress the influence of the attacks.

Secure Control for Cyber-Physical Systems with Positive Constraint Under DoS Attack

Secure Control for Cyber-Physical Systems with Positive Constraint Under DoS Attack

Derivation and Hardware-in-the-loop Testing for a Road Tunnel Controller

Developing supervisory controllers for cyber-physical systems is a difficult and error-prone process. The formal method of supervisor synthesis allows engineers to obtain a correct-by-construction supervisor from a model of the plant and a model of the requirements. Validation, however, remains an important step in this design process. Model simulation is often used as a first validation method, but this lacks aspects related to the execution of the supervisor such as the operating semantics and interfaces with external subsystems. To incorporate these aspects, hardware-in-the-loop (HIL) simulation is used. This paper describes the process of deriving an implementable controller from a synthesized supervisor, implementing it in a HIL setup, and validating it using HIL tests. Specifically, the paper focuses on the design of resource controllers, a relaxation in the confluence check for implementable controllers, and the process of automatically generating and optimizing PLC code. Furthermore, a real-life case study is presented in which a supervisory controller is derived and implemented for the Swalmen tunnel.

Security Control of Cyber-Physical Systems with Cyber Attacks and Mixed Time-Delays

This article is concerned with the security control problem for discrete-time cyber-physical systems with both distributed state delay and time-varying input delay under false data injection attacks. Firstly, by introducing mixed delay and network attack, a polyhedron model is proposed to describe the nonlinear input caused by cyber attacks. Secondly, combined with a piecewise Lyapunov functional and some summation inequalities, sufficient conditions for the exponential stability of the closed-loop system are derived by using linear matrix inequality approach, and the design process of the security controller is given. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

On the Stability of Cyber-Physical Control Systems With Sensor Multiplicative Attacks

To understand the impact of cyber-attacks to sensors in control systems, we present a stability analysis of a wide range of systems in this paper. Based on Lyapunov stability analysis, we formulate an optimization problem with constraints in the form of a set of linear matrix inequalities to find conservative bounds of stability related to the attacks, which can be analyzed simultaneously or one at the time. When considering the attacks one at the time, with the proposed formulation, we can find the most vulnerable output in the system, which can help the designer (i.e., the defender) to understand how to make the system more secure in case of multiplicative attacks on sensors. We show the effectiveness of our analysis with simulations based on the three tanks benchmark system.

Safe Reinforcement Learning Algorithm and Its Application in Intelligent Control for CPS

PDF HTML XML Export Cite reminder Safe Reinforcement Learning Algorithm and Its Application in Intelligent Control for CPS DOI: 10.21655/ijsi.1673-7288.00284 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:The design of a safe controller for a Cyber-Physical System (CPS) is a hot research topic. The existing safety controller design based on formal methods has problems such as excessive reliance on models and poor scalability. Intelligent control based on Deep Reinforcement Learning (DRL) can handle high-dimensional nonlinear complex systems and uncertain systems and is becoming a very promising CPS control technology, but it lacks safety guarantees. This study addresses the safety issues of Reinforcement Learning (RL) control by analyzing a typical case of an industrial oil pump control system and carries out research on a Safe Reinforcement Learning (SRL) algorithm and intelligent control application. First, the SRL issue of the industrial oil pump control system is formalized, and a simulation environment of the oil pump is built. Then, by designing the structure and activation function of the output layer, an oil pump controller in the form of a neural network is constructed to satisfy the linear inequality constraints of the on-off operations of the oil pump. Finally, in order to better balance the safety and optimality control objectives, a new SRL algorithm is designed based on the Augmented Lagrange Multiplier (ALM) method. A comparative experiment on the industrial oil pump shows that the controller synthesized by the proposed algorithm surpasses existing similar algorithms both in safety and optimality. During the evaluation, the neural network controllers synthesized in this study pass rigorous formal verification with a probability of 90%. Meanwhile, compared with the theoretically optimal controller, neural network controllers achieve an optimal objective value loss of 2%. The proposed method is expected to be applied in more scenarios, and the case study scheme may provide a reference for other researchers in the field of safe intelligent control and formal verification. Reference Related Cited by

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Supervisory controller synthesis and implementation for safety PLCs

The development of supervisory controllers for cyber-physical systems is a laborious and error-prone process. Supervisor synthesis enables control designers to automatically synthesize a correct-by-construction supervisor from a model of the plant combined with a model of the control requirements. From the supervisor model, controller code can be generated which is suitable for the implementation on a programmable logic controller (PLC). Supervisors for industrial systems that operate in close proximity to humans have to adhere to strict safety standards. To achieve these standards, safety PLCs (SPLCs) are used. For SPLC implementation, the supervisor has to be split into a regular part and a safety part. In previous work, a method is proposed to automatically split a supervisor model for this purpose. The method assumes that the provided plant model is a collection of finite automata. In this paper, the extension to extended finite automata is described. Additionally, guidelines are provided for modeling the plant and the requirements to achieve a favorable splitting. A case study on a rotating bridge is elaborated which has been used to validate the method. The case study spans all development steps, including the implementation of the resulting supervisor to control the real bridge.