Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
Summary: This paper presents a new framework for opacity in distributed state estimation, introduces two opacity-enhancing algorithms, and establishes necessary and sufficient conditions to ensure the secrecy of the system's state.
Article
Automation & Control Systems
Camilla Fioravanti, Stefano Panzieri, Gabriele Oliva
Summary: In this paper, the focus is on the property of negativizability in linear and time-invariant systems. The authors show that this property can be useful in solving distributed estimation and control problems in Cyber-Physical Systems (CPS). They provide a characterization of the negativizability problem, develop necessary and sufficient conditions for its solution, and demonstrate the benefits of this property through simulation experiments.
Article
Automation & Control Systems
Yan Liu, Tao Li, Bo-Chao Zheng, Mouquan Shen
Summary: In this article, a distributed resilient state estimation scheme is proposed for nonlinear systems under sensor attacks. The scheme achieves secure state estimation in a fully distributed way without requiring a majority of sensing data or global information.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
An -Yang Lu, Guang-Hong Yang
Summary: This paper addresses the problem of distributed secure state estimation in cyber-physical systems monitored by a multi-agent network with malicious agents. A novel strategy is proposed using a sort and filter approach to mitigate the impact of malicious agents. Sufficient conditions for tolerating a bounded number of malicious agents are given. Simulation results demonstrate the effectiveness of the proposed algorithms in generating correct state estimates and efficiently updating them.
Article
Computer Science, Artificial Intelligence
Yi-Gang Li, Guang-Hong Yang
Summary: This paper investigates the design of optimal stealthy switching location attacks in cyber-physical systems with multi-channels. By introducing the Kullback-Leibler divergence to characterize stealthiness, an attack strategy considering energy constraints is proposed, which only modifies a part of the channels at each time step. The results show that the attack strategy can maximize remote estimation error and maintain deception.
Article
Automation & Control Systems
Jian-Ru Huo, Xiao-Jian Li
Summary: This article focuses on designing sensor attacks to deteriorate the state estimation in cyber-physical systems. It proposes a switching attack strategy to drive the estimation error variations to the target value, while keeping the norm of innovation variations at a small level. The attack design problem is formulated as a discrete switched optimal control problem and can be solved using dynamic programming. The effectiveness of the proposed attack strategy is illustrated through a simulation example.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Mathematics, Applied
Qingyu Su, Handong Wang, Chaowei Sun, Bo Li, Jian Li
Summary: This paper addresses the issue of cyber-attacks corrupting states of cyber-physical power systems, proposing a method for secure state estimation and attack reconstruction, and designing an effective attack defense strategy.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Automation & Control Systems
Derui Ding, Qing-Long Han, Xiaohua Ge, Jun Wang
Summary: Cyber-physical systems integrate physical processes and cyber infrastructure with the help of computational resources and communication capabilities for extensive applications. However, the security of CPSs is a major concern due to vulnerabilities from the tight integration of cyber and physical components, necessitating reliable monitoring and operation techniques.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Mechanical
Shengwei Yang, Rusheng Wang, Jing Zhou, Bo Chen
Summary: This study proposes an intermediate-variable-based distributed fusion estimation method for state estimation in wind turbine systems, which uses the idea of constructing an augmented state error system and bounded recursive optimization to design local estimators and distributed fusion criteria. The method effectively improves the accuracy of wind turbine state estimation and disturbance estimation, demonstrating superiority over the Kalman fusion method in numerical simulations.
Article
Computer Science, Information Systems
Yuan-Cheng Sun, Guang-Hong Yang
Summary: This paper focuses on event-triggered remote state estimation for cyberphysical systems under malicious denial-of-service attacks. A remote estimator with intermittent observations is derived, and a novel event-triggered communication strategy is designed. Sufficient conditions for the stability of the proposed estimator are presented by constraining the attack ratio. The event-triggering parameter is designed to balance estimation performance and network resource utilization. The considered DoS attack case is more challenging compared to previous studies on remote state estimation with random packet losses. Simulation results demonstrate the efficiency of the presented method.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Jiyang Xie, Shuqian Zhu, Dawei Zhang
Summary: This paper investigates the distributed interval state estimation problem for cyber-physical systems with bounded disturbance and random stealthy attacks. An attack-resistant distributed interval observer is designed to monitor the system under random attacks. The upper- and lower-bounding estimation error systems are modeled using positive interconnected systems, and the effects of disturbance and attacks are attenuated through linear programming.
Article
Computer Science, Information Systems
Guang-Yuan Yang, Xiao-Jian Li
Summary: This paper discusses the design of complete stealthiness false data injection (FDI) attacks in cyber-physical systems with Kullback-Leiber divergence (KLD) detector. Unlike previous research on FDI attacks against KLD detector, the complete stealthiness attacks can completely eliminate their influences on innovation. The paper presents the sufficient and necessary design conditions for achieving these attacks, and introduces a data-driven attack method based on subspace identification when the attacker cannot obtain system parameters. The effectiveness of the main results is verified using a DC motor system and an unmanned ground vehicle system.
INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Biswajeet Rout, Balasubramaniam Natarajan
Summary: Modern power distribution grids face vulnerabilities due to the integration of physical system and cyber infrastructure. State estimation plays a crucial role in grid monitoring and increasing cyber-attack situational awareness. This paper proposes a distributed compressive sensing (CS) state estimation approach and analyzes the impact of measurement data loss, false data injection, replay, and neighborhood cyber-attacks.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Automation & Control Systems
Yan Liu, Guang-Hong Yang
Summary: This article investigates the event-triggered distributed state estimation problem for a class of cyber-physical systems with multiple transmission channels under denial-of-service attacks. The proposed observer-based event-triggered transmission scheme and the corresponding distributed Kalman filter improve the transmission efficiency and estimate the system states. The relationship between estimation error covariance, attack intensity, and transmission efficiency is established using the covariance intersection fusion method and the property of matrix congruent transformation rank. The considered DoS attacks compromise each channel independently and do not have to satisfy the probabilistic property of the packet loss process. The effectiveness of the proposed methods is verified through simulation results.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Mathematics, Applied
Lei Li, Wenting Wang, Qiang Ma, Kunpeng Pan, Xin Liu, Lin Lin, Jian Li
Summary: This paper focuses on the problem of cyber attack estimation and detection in cyberphysical power systems (CPPS). By modeling and designing observers, the estimation and detection of network state attacks and sensor attacks are achieved, and the feasibility is demonstrated in a simulation example.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2019)
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2019)
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2020)
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
Summary: This paper proposes two distributed state estimation protocols for static linear cyber-physical systems under Byzantine links/nodes caused by adversarial attacks. The first algorithm uses local min-switching decision to counter the influence of Byzantine links/nodes and provides necessary and sufficient conditions on network connectivity. The second algorithm uses event-triggered and minimum subset decision techniques to offer an improved low-complexity algorithm.
Article
Computer Science, Artificial Intelligence
Liwei An, Guang-Hong Yang
Summary: This article investigates power scheduling in the control of a linear plant over a wireless fading channel, establishing an optimal power allocation framework through a cost function. A new state-dependent scheduling algorithm is proposed by combining adaptive dynamic programming technique with fuzzy approximation theory to find the optimal power allocation policy. The convergence analysis shows that the proposed algorithm can approximate the optimal solution with desired precision on a compact set.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
Summary: This paper presents a new framework for opacity in distributed state estimation, introduces two opacity-enhancing algorithms, and establishes necessary and sufficient conditions to ensure the secrecy of the system's state.
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
Summary: This article investigates the problem of secure distributed optimal coordination (DOC) for multiple uncertain Euler-Lagrangian (EL) systems. It proposes a fully distributed protocol based on safety barrier certificates, convex optimization, and adaptive nonlinear control, which ensures both the global convergence and collision avoidance of the EL systems in the presence of parametric uncertainties.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
Summary: This article investigates the problem of secure state estimation for continuous-time linear systems in the presence of sparse sensor attacks. A novel supervisory state observer is proposed to address the more erratic attacks.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
Summary: This article studies a class of distributed attack strategies against state estimation of wireless sensor networks. A distributed optimization scheme is proposed to find the optimal attack strategy using precise knowledge of the measurement model and sparsity projection operation. A distributed optimization algorithm for robust sparse undetectable attacks is also proposed with the introduction of dead-zone type projection and region projection operators.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2022)
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
Summary: In this paper, a fast state estimation algorithm based on sensor types is proposed, which improves computational efficiency and accurately determines the attack location by verifying the similarity of measurement data of sensor types.
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
Summary: This article investigates a data-based distributed sensor scheduling algorithm for a wireless sensor network, which allows multiple sensor nodes to monitor different linear systems and transmit measured information over a shared wireless channel. Through the introduction of a distributed minimum subset extraction mechanism, the algorithm provides an approximate solution to minimize the H(infinity) performance index of the closed-loop system without requiring system parameter knowledge. Under sufficiently rich disturbances, the algorithm converges to the exact optimal solution.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Xiaoyu Luo, Chengcheng Zhao, Chongrong Fang, Jianping He
Summary: This paper investigates the problem of false data injection attacks in multi-agent dynamical systems and proposes FDI attack set selection algorithms to maximize the convergence error by finding the optimal subset of compromised agents.
Article
Automation & Control Systems
Nitin K. Singh, Abhisek K. Behera
Summary: In this paper, a twisting observer is proposed for robustly estimating the states of a second-order uncertain system. The observer approximates the unknown sign term for the non-measurable state with a delayed output-based switching function, and achieves the desired steady-state accuracy by controlling the delay parameter. The application of the observer to output feedback stabilization is also discussed.
Article
Automation & Control Systems
Alexander Aleksandrov
Summary: This paper investigates the absolute stability problem for positive Persidskii systems with delay, proposes a special construction method for diagonal Lyapunov-Krasovskii functionals, and derives a criterion for the existence of such functionals guaranteeing the absolute stability, as well as obtaining sufficient conditions for a family of time-delay Persidskii systems to construct a common diagonal Lyapunov-Krasovskii functional. The efficiency of the developed approaches is demonstrated through four examples.
Article
Automation & Control Systems
Noureddine Toumi, Roland Malhame, Jerome Le Ny
Summary: This paper addresses large multi-agent dynamic discrete choice problems using a linear quadratic mean field games framework. The model incorporates the features where agents have to reach a predefined set of possible destinations within a fixed time frame and running costs can become negative to simulate crowd avoidance. An upper bound on the time horizon is derived to prevent agents from escaping to infinity in finite time. The existence of a Nash equilibrium for infinite population and its epsilon-Nash property for a large but finite population are established. Simulations are conducted to explore the model behavior in various scenarios.
Article
Automation & Control Systems
Philippe Schuchert, Vaibhav Gupta, Alireza Karimi
Summary: This paper presents the design of fixed-structure controllers for the As2 and Asw synthesis problem using frequency response data. The minimization of the norm of the transfer function between the exogenous inputs and performance outputs is approximated through a convex optimization problem involving Linear Matrix Inequalities (LMIs). A general controller parametrization is used for continuous and discrete-time controllers with matrix transfer function or state-space representation. Numerical results show that the proposed data-driven method achieves performance equivalent to model-based approaches when a parametric model is available.
Correction
Automation & Control Systems
Zhijun Guo, Gang Chen
Article
Automation & Control Systems
Matteo Della Rossa, Thiago Alves Lima, Marc Jungers, Raphael M. Jungers
Summary: This paper presents new stabilizability conditions for switched linear systems with arbitrary and uncontrollable underlying switching signals. The study focuses on two specific settings: the robust case with completely unknown and unobservable active mode, and the mode-dependent case with controller depending on the current active switching mode. The technical developments are based on graph-theory tools and path-complete Lyapunov functions framework, enabling the design of robust and mode-dependent piecewise linear state-feedback controllers using directed and labeled graphs.
Article
Automation & Control Systems
Elena Petri, Romain Postoyan, Daniele Astolfi, Dragan Nesic, W. P. M. H. (Maurice) Heemels
Summary: This study investigates a scenario where a perturbed nonlinear system transmits its output measurements to a remote observer via a packet-based communication network. By designing both the observer and the local transmission policies, accurate state estimates can be obtained while only sporadically using the communication network.
Article
Automation & Control Systems
Jonas Krook, Robi Malik, Sahar Mohajerani, Martin Fabian
Summary: This paper proposes a method to synthesise controllers for cyber-physical systems subjected to disturbances, such that the controlled system satisfies specifications given as linear temporal logic formulas. The approach constructs a finite-state abstraction of the original system and synthesises a controller for the abstraction. It introduces the robust stutter bisimulation relation to account for disturbances and uncertainty, ensuring that related states have similar effects under the same controller. The paper demonstrates that the existence of a controller for the abstracted system implies the existence of a controller for the original system enforcing the linear temporal logic formula.
Article
Automation & Control Systems
Clement Chahbazian, Karim Dahia, Nicolas Merlinge, Benedicte Winter-Bonnet, Aurelien Blanc, Christian Musso
Summary: The paper derives a recursive formula of the Fisher information matrix on Lie groups and applies it to nonlinear Gaussian systems on Lie groups for testing. The proposed recursive CRLB is consistent with state-of-the-art filters and exhibits representative behavior in estimation errors. This paper provides a simple method to recursively compute the minimal variance of an estimator on matrix Lie groups, which is fundamental for implementing robust algorithms.
Article
Automation & Control Systems
Yiheng Fu, Pouria Ramazi
Summary: This study investigates the characteristics of decision fluctuations in heterogeneous populations and explores the uncertainties in imitation behavior. The findings are important for understanding the bounded rationality nature of imitation behaviors.
Article
Automation & Control Systems
Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Summary: This paper introduces a mathematical relationship between the accuracy of reduced-order linear-time invariant subsystem models and the stability and accuracy of the resulting reduced-order interconnected linear time-invariant model. This result can be used to directly translate the accuracy characteristics of the reduced-order subsystem models to the accuracy properties of the interconnected reduced-order model, or to translate accuracy requirements on the interconnected system model to accuracy requirements on subsystem models.
Article
Automation & Control Systems
Piyush Gupta, Vaibhav Srivastava
Summary: We study the optimal fidelity selection for a human operator servicing tasks in a queue, considering the trade-off between high-quality service and penalty due to increased queue length. By modeling the operator's cognitive dynamics and task fidelity, we determine the optimal policy and value function numerically, and analyze the structural properties of the optimal fidelity policy.
Article
Automation & Control Systems
Lukas Schwenkel, Alexander Hadorn, Matthias A. Mueller, Frank Allgoewer
Summary: In this work, the authors study economic model predictive control (MPC) in periodic operating conditions. They propose a method to achieve optimality by multiplying the stage cost by a linear discount factor, which is easy to implement and robust against online changes. Under certain assumptions, they prove that the resulting linearly discounted economic MPC achieves optimal asymptotic average performance and guarantees practical asymptotic stability of the optimal periodic orbit.
Article
Automation & Control Systems
Taher Ebrahim, Sankaranarayanan Subramanian, Sebastian Engell
Summary: We propose a robust nonlinear model predictive control algorithm for dynamic systems with mixed degrees of freedom. This algorithm optimizes both continuous and discrete manipulated variables, enhancing closed-loop performance. Our approach relies on a computationally efficient relaxation and integrality restoration strategy and provides sufficient conditions to establish recursive feasibility and guarantee robust closed-loop stability. The effectiveness of the approach is demonstrated through two nonlinear simulation examples.
