| 1. |
- Sasahara, Hampei, et al.
(författare)
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Distributed Design of Glocal Controllers via Hierarchical Model Decomposition
- 2023
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 68:10, s. 6146-6159
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Tidskriftsartikel (refereegranskat)abstract
- This article proposes a distributed design method of controllers with a glocal (global/local) information structure for large-scale network systems. The glocal controller of interest has a hierarchical structure, wherein a global subcontroller coordinates a set of disjoint local subcontrollers. The global subcontroller regulates interarea oscillations among subsystems, while local subcontrollers individually regulate intraarea oscillations of the respective subsystem. The distributed design of the glocal controller is addressed to enhance the scalability of controller synthesis, where the global subcontroller and all local subcontrollers are designed independently of each other. A design problem is formulated for subcontroller sets such that any combination of subcontrollers each of which belongs to its corresponding set guarantees stability of the closed-loop system. The core idea of the proposed method is to represent the original network system as a hierarchical cascaded system composed of reduced-order models representing the interarea and intraarea dynamics, referred to as hierarchical model decomposition. Distributed design is achieved by virtue of the cascade structure. The primary findings of this study are twofold. First, a tractable solution to the distributed design problem and an existence condition of the hierarchical model decomposition are presented. Second, a clustering method appropriate for the proposed framework and a robust extension are provided. Numerical examples of a power grid highlight the practical relevance of the proposed method.
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| 2. |
- Alanwar, Amr, et al.
(författare)
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Privacy-preserving set-based estimation using partially homomorphic encryption
- 2023
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Ingår i: European Journal of Control. - : Elsevier BV. - 0947-3580 .- 1435-5671. ; 71, s. 100786-
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Tidskriftsartikel (refereegranskat)abstract
- The set-based estimation has gained a lot of attention due to its ability to guarantee state enclosures for safety-critical systems. However, collecting measurements from distributed sensors often requires out-sourcing the set-based operations to an aggregator node, raising many privacy concerns. To address this problem, we present set-based estimation protocols using partially homomorphic encryption that pre-serve the privacy of the measurements and sets bounding the estimates. We consider a linear discrete-time dynamical system with bounded modeling and measurement uncertainties. Sets are represented by zonotopes and constrained zonotopes as they can compactly represent high-dimensional sets and are closed under linear maps and Minkowski addition. By selectively encrypting parameters of the set repre-sentations, we establish the notion of encrypted sets and intersect sets in the encrypted domain, which enables guaranteed state estimation while ensuring privacy. In particular, we show that our protocols achieve computational privacy using the cryptographic notion of computational indistinguishability. We demonstrate the efficiency of our approach by localizing a real mobile quadcopter using ultra-wideband wireless devices.
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| 3. |
- Alisic, Rijad
(författare)
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Defense of Cyber-Physical Systems Against Learning-based Attackers
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cyberattacks against critical infrastructures pose a serious threat to society, as they can have devastating consequences on the economy, security, or public health. These infrastructures rely on a large network of cyber components, such as sensors, controllers, computers, and communication devices, to monitor and control their physical processes. An adversary can exploit the vulnerabilities in these cyber components to gain access to the system and manipulate its behavior or functionality.This thesis proposes methods that can be employed as a first line of defense against such attacks for Cyber-Physical Systems. In the first part of the thesis, we consider how uninformed attackers can learn to attack a Cyber-Physical System by eavesdropping through the cyber component. By learning to manipulate the plant, the attacker could figure out how to destroy the physical system before it is too late or completely take it over without raising any alarms. Stopping the attacker at the learning stage would force the attacker to act obliviously, increasing the chances of detecting them.We analyze how homomorphic encryption, a technique that allows computation on encrypted data, hinders an attacker's learning process and reduces its capabilities to attack the system. Specifically, we show that an attacker must solve challenging lattice problems to find attacks that are difficult to detect. Additionally, we show how the detection probability is affected by the attacker's solution to the problems and what parameters of the encryption scheme can be tweaked to increase the detection probability. We also develop a novel method that enables anomaly detection over homomorphically encrypted data without revealing the actual signals to the detector, thereby discouraging attackers from launching attacks on the detector. The detection can be performed using a hypothesis test. However, special care must be taken to ensure that fresh samples are used to detect changes from nominal behavior. We also explore how the adversary can try to evade detection using the same test and how the system can be designed to make detection easier for the defender and more challenging for the attacker.In the second part of the thesis, we study how information leakage about changes in the system depends on the system's dynamics. We use a mathematical tool called the Hammersley-Chapman-Robbins lower bound to measure how much information is leaked and how to minimize it. Specifically, we study how structured input sequences, which we call events, can be obtained through the output of a dynamical system and how this information can be hidden by adding noise or changing the inputs. The system’s speed and sensor locations affect how much information is leaked. We also consider balancing the system’s performance and privacy when using optimal control. Finally, we show how to estimate when the adversary’s knowledge of the event becomes accurate enough to launch an attack and how to change the system before that happens. These results are then used to aid the operator in detecting privacy vulnerabilities when designing a Cyber-Physical System, which increases the overall security when removed.
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| 4. |
- Niazi, Muhammad Umar B., et al.
(författare)
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Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems
- 2023
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Ingår i: 2023 American Control Conference , ACC. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3048-3055
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Konferensbidrag (refereegranskat)abstract
- Designing Luenberger observers for nonlinear systems involves the challenging task of transforming the state to an alternate coordinate system, possibly of higher dimensions, where the system is asymptotically stable and linear up to output injection. The observer then estimates the system's state in the original coordinates by inverting the transformation map. However, finding a suitable injective transformation whose inverse can be derived remains a primary challenge for general nonlinear systems. We propose a novel approach that uses supervised physics-informed neural networks to approximate both the transformation and its inverse. Our method exhibits superior generalization capabilities to contemporary methods and demonstrates robustness to both neural network's approximation errors and system uncertainties.
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| 5. |
- Pare, Philip E., et al.
(författare)
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Multilayer SIS Model With an Infrastructure Network
- 2023
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Ingår i: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; 10:1, s. 295-307
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we develop a layered networked spread model for a susceptible-infected-susceptible pathogen-borne disease spreading over a human contact network and an infrastructure network, and refer to it as a layered networked susceptible-infected-water-susceptible model (SIWS). The "W" in SIWS represents any infrastructure network contamination, not necessarily restricted to a water distribution network. We identify sufficient conditions for the existence, uniqueness, and stability of various equilibria of the aforementioned model. Further, we study an observability problem, where, assuming that the measurements of the pathogen levels in the infrastructure network are available, we provide a necessary and sufficient condition for estimation of the sickness levels of the nodes in the human contact network. Our results are illustrated through an in-depth set of simulations.
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| 6. |
- She, Baike, et al.
(författare)
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Epidemics Spread Over Networks : Influence of Infrastructure and Opinions
- 2023
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Ingår i: Cyber–Physical–Human Systems. - : Wiley. ; , s. 429-456
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter, we focus on epidemics spreading over networks. Over the last several decades, researchers across multiple communities have studied epidemics, among which are the classical epidemic models that assume that the population is well mixed. These classical models have been shown to be useful for studying epidemic outbreaks in densely connected populations. However, motivated by the need to understand epidemics at a more fine-grained level (encompassing heterogeneity in individual characteristics or contacts), networked models of epidemic spread have started to gain significant attention in recent years. In this chapter, we consider two types of epidemic spreading models that capture the notation of human-in-the-plant. We first provide a background on modeling, analysis, and applications of networked epidemic models. We show that networked epidemic models are capable of tracing the origin of an outbreak, which aids in developing control strategies for eradicating an epidemic. In the second part of this chapter, we discuss how some cyber–physical–human systems (CPHS) can propagate, or hinder, the spread of epidemics over networks. CPHS are composed of a series of interconnected systems that interact with one another. As such, these are extremely appealing for modeling, analyzing, and eradicating epidemics by capturing the impact of infrastructure, economy, and human factors. Next, we highlight two of our recent works that consider the combination of CPHS with epidemics spreading over networks. In the first work, we model an epidemic spreading process over connected communities by coupling the opinions of these communities over a social network. We analyze the influence of the opinions regarding the outbreak on the epidemic spreading process. In the second work, we consider an epidemic spreading process over connected communities with a shared resource (e.g. a water resource, a supermarket, and a metro station). We model the epidemic spreading process by considering the influence of the shared resource and show that the shared resource is critical in determining the shape of the epidemic (i.e. amount of population infected, hospitalized, recovered, etc.). Finally, we conclude by providing insights on potential future research directions.
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| 7. |
- Stefansson, Elis
(författare)
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Complexity-aware Decision-making with Applications to Large-scale and Human-in-the-loop Systems
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis considers control systems governed by autonomous decision-makers and humans. We formalise and compute low-complex control policies with applications to large-scale systems, and propose human interaction models for controllers to compute interaction-aware decisions.In the first part of the thesis, we consider complexity-aware decision-making, formalising the complexity of control policies and constructing algorithms that compute low-complexity control policies. More precisely, first, we consider large-scale control systems given by hierarchical finite state machines (HFSMs) and present a planning algorithm for such systems that exploits the hierarchy to compute optimal policies efficiently. The algorithm can also handle changes in the system with ease. We prove these properties and conduct simulations on HFSMs with up to 2 million states, including a robot application, where our algorithm outperforms both Dijkstra's algorithm and Contraction Hierarchies. Second, we present a planning objective for control systems modelled as finite state machines yielding an explicit trade-off between a policy's performance and complexity. We consider Kolmogorov complexity since it captures the ultimate compression of an object on a universal Turing machine. We prove that this trade-off is hard to optimise in the sense that dynamic programming is infeasible. Nonetheless, we present two heuristic algorithms obtaining low-complexity policies and evaluate the algorithms on a simple navigation task for a mobile robot, where we obtain low-complexity policies that concur with intuition. In the second part of the thesis, we consider human-in-the-loop systems and predict human decision-making in such systems. First, we look at how the interaction between a robot and a human in a control system can be predicted using game theory, focusing on an autonomous truck platoon interacting with a human-driven car. The interaction is modelled as a hierarchical dynamic game, where the hierarchical decomposition is temporal with a high-fidelity tactical horizon predicting immediate interactions and a low-fidelity strategic horizon estimating long-term behaviour. The game enables feasible computations validated through simulations yielding situation-aware behaviour with natural and safe interactions. Second, we seek models to explain human decision-making, focusing on driver overtaking scenarios. The overtaking problem is formalised as a decision problem with perceptual uncertainty. We propose and numerically analyse risk-agnostic and risk-aware decision models, judging if an overtaking is desirable. We show how a driver's decision time and confidence level can be characterised through two model parameters, which collectively represent human risk-taking behaviour. We detail an experimental testbed for evaluating the decision-making process in the overtaking scenario and present some preliminary experimental results from two human drivers.
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| 8. |
- Yi, Xinlei, et al.
(författare)
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Sublinear and Linear Convergence of Modified ADMM for Distributed Nonconvex Optimization
- 2023
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Ingår i: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; 10:1, s. 75-86
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we consider distributed nonconvex optimization over an undirected connected network. Each agent can only access to its own local nonconvex cost function and all agents collaborate to minimize the sum of these functions by using local information exchange. We first propose a modified alternating direction method of multipliers (ADMM) algorithm. We show that the proposed algorithm converges to a stationary point with the sublinear rate O(1/T) if each local cost function is smooth and the algorithm parameters are chosen appropriately. We also show that the proposed algorithm linearly converges to a global optimum under an additional condition that the global cost function satisfies the Polyak-Łojasiewicz condition, which is weaker than the commonly used conditions for showing linear convergence rates including strong convexity. We then propose a distributed linearized ADMM (L-ADMM) algorithm, derived from the modified ADMM algorithm, by linearizing the local cost function at each iteration. We show that the L-ADMM algorithm has the same convergence properties as the modified ADMM algorithm under the same conditions. Numerical simulations are included to verify the correctness and efficiency of the proposed algorithms.
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