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Prof. Dr. Quanyan Zhu
Department of Electrical and Computer Engineering, New York University-Tandon School of Engineering, Brooklyn, NY 11201, USA

Basic Info

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Research Keywords & Expertise

0 Game Theory
0 Internet of Things
0 Cybersecurity
0 Resilient and secure cyber-physical-human systems
0 AI methods in automation

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Internet of Things
Game Theory
Cybersecurity
Critical infrastructures
Resilient and secure cyber-physical-human systems
Resource allocation and economics
Decentralized control and decision-making

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Journal article
Published: 23 April 2021 in IEEE Transactions on Signal and Information Processing over Networks
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Optimal curing strategy of suppressing competing epidemics spreading over complex networks is a critical issue. In this paper, we first establish a framework to capture the coupling between two epidemics, and then analyze the system's equilibrium states by categorizing them into three classes, and deriving their stability conditions. The designed curing strategy globally optimizes the trade-off between the curing cost and the severity of epidemics in the network. In addition, we provide structural results on the predictability of epidemic spreading by showing the existence and uniqueness of the solution. We also demonstrate the robustness of curing strategy by showing the continuity of epidemic severity with respect to the applied curing effort. A gradient descent algorithm based on a fixed-point iterative scheme is proposed to find the optimal curing strategy. Depending on the system parameters, the curing strategy can lead to switching between equilibria of the epidemic network as the control cost varies. Finally, we use case studies to corroborate and illustrate the obtained theoretical results.

ACS Style

Juntao Chen; Yunhan Huang; Rui Zhang; Quanyan Zhu. Optimal Curing Strategy for Competing Epidemics Spreading Over Complex Networks. IEEE Transactions on Signal and Information Processing over Networks 2021, 7, 294 -308.

AMA Style

Juntao Chen, Yunhan Huang, Rui Zhang, Quanyan Zhu. Optimal Curing Strategy for Competing Epidemics Spreading Over Complex Networks. IEEE Transactions on Signal and Information Processing over Networks. 2021; 7 ():294-308.

Chicago/Turabian Style

Juntao Chen; Yunhan Huang; Rui Zhang; Quanyan Zhu. 2021. "Optimal Curing Strategy for Competing Epidemics Spreading Over Complex Networks." IEEE Transactions on Signal and Information Processing over Networks 7, no. : 294-308.

Conference paper
Published: 22 December 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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This paper studies a special class of games, which enables the players to leverage the information from a dataset to play the game. However, in an adversarial scenario, the dataset may not be trustworthy. We propose a distributionally robust formulation to introduce robustness against the worst-case scenario and tackle the curse of the optimizer. By applying Wasserstein distance as the distribution metric, we show that the game considered in this work is a generalization of the robust game and data-driven empirical game. We also show that as the number of data points in the dataset goes to infinity, the game considered in this work boils down to a Nash game. Moreover, we present the proof of the existence of distributionally robust equilibria and a tractable mathematical programming approach to solve for such equilibria.

ACS Style

Guanze Peng; Tao Zhang; Quanyan Zhu. A Data-Driven Distributionally Robust Game Using Wasserstein Distance. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 405 -421.

AMA Style

Guanze Peng, Tao Zhang, Quanyan Zhu. A Data-Driven Distributionally Robust Game Using Wasserstein Distance. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():405-421.

Chicago/Turabian Style

Guanze Peng; Tao Zhang; Quanyan Zhu. 2020. "A Data-Driven Distributionally Robust Game Using Wasserstein Distance." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 405-421.

Conference paper
Published: 22 December 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Zero-sum games have been used to model cybersecurity scenarios between an attacker and a defender. However, unknown and uncertain environments have made it difficult to rely on a prescribed zero-sum game to capture the interactions between the players. In this work, we aim to estimate and recover an unknown matrix game that encodes the uncertainties of nature and opponent based on the knowledge of historical games and the current observations of game outcomes. The proposed approach effectively transfers the past experiences that are encoded as expert games to estimate and inform future game plays. We formulate the game knowledge transfer and estimation problem as a sequential least-square problem. We characterize the structural properties of the problem and show that the non-convex problem has well-behaved gradient and Hessian under mild assumptions. We propose gradient-based methods to enable dynamic and adaptive estimation of the unknown game. A case study is used to corroborate the results and illustrate the behavior of the proposed algorithm.

ACS Style

Yunian Pan; Guanze Peng; Juntao Chen; Quanyan Zhu. MASAGE: Model-Agnostic Sequential and Adaptive Game Estimation. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 365 -384.

AMA Style

Yunian Pan, Guanze Peng, Juntao Chen, Quanyan Zhu. MASAGE: Model-Agnostic Sequential and Adaptive Game Estimation. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():365-384.

Chicago/Turabian Style

Yunian Pan; Guanze Peng; Juntao Chen; Quanyan Zhu. 2020. "MASAGE: Model-Agnostic Sequential and Adaptive Game Estimation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 365-384.

Journal article
Published: 14 August 2020 in IEEE Transactions on Control of Network Systems
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A cyber security problem in a networked system formulated as a resilient graph problem based on a game-theoretic approach is considered. The connectivity of the underlying graph of the network system is reduced by an attacker who removes some of the edges whereas the defender attempts to recover them. Both players are subject to energy constraints so that their actions are restricted and cannot be performed continuously. For this two-stage game, we characterize the optimal strategies for the attacker and the defender in terms of edge connectivity and the number of connected components of the graph. The resilient graph game is then applied to a multi-agent consensus problem, where the game is played repeatedly over time. We study how the attacks and the recovery on the edges affect the consensus process. Finally, we also provide numerical simulation to illustrate the results.

ACS Style

Yurid Nugraha; Ahmet Cetinkaya; Tomohisa Hayakawa; Hideaki Ishii; Quanyan Zhu. Dynamic Resilient Network Games With Applications to Multiagent Consensus. IEEE Transactions on Control of Network Systems 2020, 8, 246 -259.

AMA Style

Yurid Nugraha, Ahmet Cetinkaya, Tomohisa Hayakawa, Hideaki Ishii, Quanyan Zhu. Dynamic Resilient Network Games With Applications to Multiagent Consensus. IEEE Transactions on Control of Network Systems. 2020; 8 (1):246-259.

Chicago/Turabian Style

Yurid Nugraha; Ahmet Cetinkaya; Tomohisa Hayakawa; Hideaki Ishii; Quanyan Zhu. 2020. "Dynamic Resilient Network Games With Applications to Multiagent Consensus." IEEE Transactions on Control of Network Systems 8, no. 1: 246-259.

Journal article
Published: 07 July 2020 in IEEE Control Systems Letters
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This letter considers a hybrid risk measure for decision-making under uncertainties that tradeoffs between the solutions obtained from the robust optimization and the stochastic optimization techniques. In the proposed framework, the risk measure is shown to satisfy the properties of coherent risk measures. We can control the level of guaranteed robustness using a parameter. We formulate the stochastic and robust optimization problem under the proposed risk measure and show its equivalent formulation and sensitivity result. We introduce the sample approximation of our technique by combining scenario program and sample average approximation, making our framework amenable for practical usage. We present a supervised learning problem as a case study to corroborate our results and show the implications of the proposed method in machine learning.

ACS Style

Shutian Liu; Quanyan Zhu. Robust and Stochastic Optimization With a Hybrid Coherent Risk Measure With an Application to Supervised Learning. IEEE Control Systems Letters 2020, 5, 965 -970.

AMA Style

Shutian Liu, Quanyan Zhu. Robust and Stochastic Optimization With a Hybrid Coherent Risk Measure With an Application to Supervised Learning. IEEE Control Systems Letters. 2020; 5 (3):965-970.

Chicago/Turabian Style

Shutian Liu; Quanyan Zhu. 2020. "Robust and Stochastic Optimization With a Hybrid Coherent Risk Measure With an Application to Supervised Learning." IEEE Control Systems Letters 5, no. 3: 965-970.

Journal article
Published: 24 June 2020 in IEEE Control Systems Letters
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In this letter, we study the stabilization of two interdependent Markov jump linear systems (MJLSs) with partial information, where the interdependency arises as the transition of the mode of one system depends on the states of the other system. First, we formulate a framework for the two interdependent MJLSs to capture the interactions between various entities in the system, where the modes of the system cannot be observed directly. Instead, a signal which contains information of the modes can be observed. Then, depending on the scope of the available system state information (global or local), we design centralized and distributed controllers, respectively, that can stochastically stabilize the overall interdependent MJLS. In addition, we derive the sufficient stabilization conditions for the system under both types of information structure. Finally, we use a numerical example to illustrate the effectiveness of the designed controllers.

ACS Style

Guanze Peng; Juntao Chen; Quanyan Zhu. Distributed Stabilization of Two Interdependent Markov Jump Linear Systems With Partial Information. IEEE Control Systems Letters 2020, 5, 713 -718.

AMA Style

Guanze Peng, Juntao Chen, Quanyan Zhu. Distributed Stabilization of Two Interdependent Markov Jump Linear Systems With Partial Information. IEEE Control Systems Letters. 2020; 5 (2):713-718.

Chicago/Turabian Style

Guanze Peng; Juntao Chen; Quanyan Zhu. 2020. "Distributed Stabilization of Two Interdependent Markov Jump Linear Systems With Partial Information." IEEE Control Systems Letters 5, no. 2: 713-718.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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Nowadays, epidemic models provide an appropriate tool to describe the propagation of biological viruses in human or animal populations, rumors and misinformation in social networks, and malware in both computer and ad hoc networks. It is common that there are multiple types of malware infecting a network of computing devices, and different messages can spread over the social network. Information spreading and virus propagation are interdependent processes. To capture their independencies, we integrate two epidemic models into one holistic framework, known as the modified Susceptible-Warned-Infected-Recovered-Susceptible (SWIRS) model. The first epidemic model describes the information spreading regarding the risk of malware attacks and possible preventive procedures. The second one describes the propagation of multiple viruses over the network of devices. To minimize the impact of the virus spreading and improve the protection of the networks, we consider an optimal control problem with two types of control strategies: information spreading among healthy nodes and the treatment of infected nodes. We obtain the structure of optimal control strategies and study the condition of epidemic outbreaks. The main results are extended to the case of the network of two connected clusters. Numerical examples are used to corroborate the theoretical findings.

ACS Style

Vladislav Taynitskiy; Elena Gubar; Denis Fedyanin; Ilya Petrov; Quanyan Zhu. Optimal Control of Joint Multi-Virus Infection and Information Spreading. IFAC-PapersOnLine 2020, 53, 6650 -6655.

AMA Style

Vladislav Taynitskiy, Elena Gubar, Denis Fedyanin, Ilya Petrov, Quanyan Zhu. Optimal Control of Joint Multi-Virus Infection and Information Spreading. IFAC-PapersOnLine. 2020; 53 (2):6650-6655.

Chicago/Turabian Style

Vladislav Taynitskiy; Elena Gubar; Denis Fedyanin; Ilya Petrov; Quanyan Zhu. 2020. "Optimal Control of Joint Multi-Virus Infection and Information Spreading." IFAC-PapersOnLine 53, no. 2: 6650-6655.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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A cybersecurity problem for a multi-agent consensus problem is investigated through a dynamic game formulation. Specifically, we consider a game repeatedly played between a jamming attacker and a defender. The attacker attempts to jam the links between a number of agents to delay their consensus. On the other hand, the defender tries to maintain the connection between agents by attempting to recover some of the jammed links with the goal of achieving faster consensus. In each game, the players decide which links to attack/recover and for how long to continue doing so based on a Lyapunov-like function representing the largest difference between the states of the agents. We analyze the subgame perfect equilibrium of the game and obtain an upper bound of the consensus time that is influenced by the strategies of the players. The results are illustrated with a numerical example.

ACS Style

Yurid Nugraha; Ahmet Cetinkaya; Tomohisa Hayakawa; Hideaki Ishii; Quanyan Zhu. Dynamic Resilient Graph Games for State-Dependent Jamming Attacks Analysis on Multi-Agent Systems. IFAC-PapersOnLine 2020, 53, 3421 -3426.

AMA Style

Yurid Nugraha, Ahmet Cetinkaya, Tomohisa Hayakawa, Hideaki Ishii, Quanyan Zhu. Dynamic Resilient Graph Games for State-Dependent Jamming Attacks Analysis on Multi-Agent Systems. IFAC-PapersOnLine. 2020; 53 (2):3421-3426.

Chicago/Turabian Style

Yurid Nugraha; Ahmet Cetinkaya; Tomohisa Hayakawa; Hideaki Ishii; Quanyan Zhu. 2020. "Dynamic Resilient Graph Games for State-Dependent Jamming Attacks Analysis on Multi-Agent Systems." IFAC-PapersOnLine 53, no. 2: 3421-3426.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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This paper investigates a class of optimal control problems associated with Markov processes with local state information. The decision-maker has only a local access to a subset of a state vector information as often encountered in decentralized control problems in multi-agent systems. Under this information structure, part of the state vector cannot be observed. We leverage ab initio principles and find a new form of Bellman equations to characterize the optimal policies of the control problem under local information structures. The dynamic programming solutions feature a mixture of dynamics associated unobservable state components and the local state-feedback policy based on the observable local information. We further characterize the optimal local-state feedback policy using linear programming methods. To reduce the computational complexity of the optimal policy, we propose an approximate algorithm based on virtual beliefs to find a sub-optimal policy. We show the performance bounds on the sub-optimal solution and corroborate the results with numerical case studies.

ACS Style

Guanze Peng; Veeraruna Kavitha; Quanyan Zhu. On Optimal Control of Discounted Cost Infnite-Horizon Markov Decision Processes Under Local State Information Structures. IFAC-PapersOnLine 2020, 53, 6881 -6886.

AMA Style

Guanze Peng, Veeraruna Kavitha, Quanyan Zhu. On Optimal Control of Discounted Cost Infnite-Horizon Markov Decision Processes Under Local State Information Structures. IFAC-PapersOnLine. 2020; 53 (2):6881-6886.

Chicago/Turabian Style

Guanze Peng; Veeraruna Kavitha; Quanyan Zhu. 2020. "On Optimal Control of Discounted Cost Infnite-Horizon Markov Decision Processes Under Local State Information Structures." IFAC-PapersOnLine 53, no. 2: 6881-6886.

Journal article
Published: 25 December 2019 in IEEE Transactions on Control of Network Systems
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Multiple heterogeneous mobile autonomous system (MAS) networks can be integrated together as a multi-layer MAS network to offer holistic services. The network connectivity of multi-layer MAS plays an important role in the information exchange between agents within and across different layers of the network. In this paper, we establish a games-in-games framework to capture the uncoordinated nature of decision makings under adversarial environment at different layers. Specifically, each network operator controls the mobile agents in his own subnetwork and designs a secure strategy to maximize the global network connectivity by considering the behavior of jamming attackers that aim to disconnect the network. The solution concept of meta-equilibrium is proposed to characterize the system-of-systems behavior of the autonomous agents. For online implementation of the control, we design a resilient algorithm that improves the network algebraic connectivity iteratively. We show that the designed algorithm converges to a meta-equilibrium asymptotically. Finally, we use case studies of a two-layer MAS network to corroborate the results.

ACS Style

Juntao Chen; Quanyan Zhu. Control of Multilayer Mobile Autonomous Systems in Adversarial Environments: A Games-in-Games Approach. IEEE Transactions on Control of Network Systems 2019, 7, 1056 -1068.

AMA Style

Juntao Chen, Quanyan Zhu. Control of Multilayer Mobile Autonomous Systems in Adversarial Environments: A Games-in-Games Approach. IEEE Transactions on Control of Network Systems. 2019; 7 (3):1056-1068.

Chicago/Turabian Style

Juntao Chen; Quanyan Zhu. 2019. "Control of Multilayer Mobile Autonomous Systems in Adversarial Environments: A Games-in-Games Approach." IEEE Transactions on Control of Network Systems 7, no. 3: 1056-1068.

Preprint
Published: 06 December 2019
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Strictly speaking, Newton's second law of motion is only an approximation of the so-called relativistic dynamics, i.e., Einstein's modification of the second law based on his theory of special relativity. Although the approximation is almost exact when the velocity of the dynamical system is far less than the speed of light, the difference will become larger and larger (and will eventually go to infinity) as the velocity approaches the speed of light. Correspondingly, feedback control of such dynamics should also take this modification into consideration (though it will render the system nonlinear), especially when the velocity is relatively large. Towards this end, we start this paper by studying the state-space representation of the relativistic dynamics. We then investigate on how to employ the feedback linearization approach for such relativistic dynamics, based upon which an additional linear controller may then be designed. As such, the feedback linearization together with the linear controller compose the overall relativistic feedback control law. We also provide further discussions on, e.g., controllability, state feedback and output feedback, as well as PID control, in the relativistic setting.

ACS Style

Song Fang; Quanyan Zhu. Relativistic Control: Feedback Control of Relativistic Dynamics. 2019, 1 .

AMA Style

Song Fang, Quanyan Zhu. Relativistic Control: Feedback Control of Relativistic Dynamics. . 2019; ():1.

Chicago/Turabian Style

Song Fang; Quanyan Zhu. 2019. "Relativistic Control: Feedback Control of Relativistic Dynamics." , no. : 1.

Editorial
Published: 20 November 2019 in Computers & Security
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ACS Style

Stefan Rass; Quanyan Zhu. Computer & security special issue editorial. Computers & Security 2019, 89, 101678 .

AMA Style

Stefan Rass, Quanyan Zhu. Computer & security special issue editorial. Computers & Security. 2019; 89 ():101678.

Chicago/Turabian Style

Stefan Rass; Quanyan Zhu. 2019. "Computer & security special issue editorial." Computers & Security 89, no. : 101678.

Journal article
Published: 30 October 2019 in IEEE Transactions on Cognitive Communications and Networking
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ACS Style

Manjesh Kumar Hanawal; Yezekael Hayel; Quanyan Zhu. Effective Utilization of Licensed and Unlicensed Spectrum in Large Scale Ad Hoc Networks. IEEE Transactions on Cognitive Communications and Networking 2019, 6, 618 -630.

AMA Style

Manjesh Kumar Hanawal, Yezekael Hayel, Quanyan Zhu. Effective Utilization of Licensed and Unlicensed Spectrum in Large Scale Ad Hoc Networks. IEEE Transactions on Cognitive Communications and Networking. 2019; 6 (2):618-630.

Chicago/Turabian Style

Manjesh Kumar Hanawal; Yezekael Hayel; Quanyan Zhu. 2019. "Effective Utilization of Licensed and Unlicensed Spectrum in Large Scale Ad Hoc Networks." IEEE Transactions on Cognitive Communications and Networking 6, no. 2: 618-630.

Conference paper
Published: 23 October 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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This paper studies reinforcement learning (RL) under malicious falsification on cost signals and introduces a quantitative framework of attack models to understand the vulnerabilities of RL. Focusing on Q-learning, we show that Q-learning algorithms converge under stealthy attacks and bounded falsifications on cost signals. We characterize the relation between the falsified cost and the Q-factors as well as the policy learned by the learning agent which provides fundamental limits for feasible offensive and defensive moves. We propose a robust region in terms of the cost within which the adversary can never achieve the targeted policy. We provide conditions on the falsified cost which can mislead the agent to learn an adversary’s favored policy. A numerical case study of water reservoir control is provided to show the potential hazards of RL in learning-based control systems and corroborate the results.

ACS Style

Yunhan Huang; Quanyan Zhu. Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 217 -237.

AMA Style

Yunhan Huang, Quanyan Zhu. Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():217-237.

Chicago/Turabian Style

Yunhan Huang; Quanyan Zhu. 2019. "Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 217-237.

Preprint
Published: 12 October 2019
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In this paper, we derive generic bounds on the maximum deviations in prediction errors for sequential prediction via an information-theoretic approach. The fundamental bounds are shown to depend only on the conditional entropy of the data point to be predicted given the previous data points. In the asymptotic case, the bounds are achieved if and only if the prediction error is white and uniformly distributed.

ACS Style

Song Fang; Quanyan Zhu. Generic Bounds on the Maximum Deviations in Sequential Prediction: An Information-Theoretic Analysis. 2019, 1 .

AMA Style

Song Fang, Quanyan Zhu. Generic Bounds on the Maximum Deviations in Sequential Prediction: An Information-Theoretic Analysis. . 2019; ():1.

Chicago/Turabian Style

Song Fang; Quanyan Zhu. 2019. "Generic Bounds on the Maximum Deviations in Sequential Prediction: An Information-Theoretic Analysis." , no. : 1.

Journal article
Published: 25 September 2019 in IEEE Transactions on Automatic Control
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As a subclass of stochastic differential games with algebraic constraints, this article studies dynamic noncooperative games where the dynamics are described by Markov jump differential-algebraic equations (DAEs). Theoretical tools, which require computing the extended generator and deriving Hamilton-Jacobi-Bellman (HJB) equation for Markov jump DAEs, are developed. These fundamental results lead to pure feedback optimal strategies to compute the Nash equilibrium in noncooperative setting. In case of quadratic cost and linear dynamics, these strategies are obtained by solving coupled Riccatilike differential equations. Under an appropriate stabilizability assumption on system dynamics, these differential equations reduce to coupled algebraic Riccati equations when the cost functionals are considered over infinite-horizon. As a first casestudy, the application of our results is studied in the context of an economic system where different suppliers aim to maximize their profits subject to the market demands and fluctuations in operating conditions. The second case-study refers to the conventional problem of robust control for randomly switching linear DAEs, which can be formulated as a two-player zero sum game and is solved using the results developed in this paper.

ACS Style

Aneel Tanwani; Quanyan Zhu. Feedback Nash Equilibrium for Randomly Switching Differential–Algebraic Games. IEEE Transactions on Automatic Control 2019, 65, 3286 -3301.

AMA Style

Aneel Tanwani, Quanyan Zhu. Feedback Nash Equilibrium for Randomly Switching Differential–Algebraic Games. IEEE Transactions on Automatic Control. 2019; 65 (8):3286-3301.

Chicago/Turabian Style

Aneel Tanwani; Quanyan Zhu. 2019. "Feedback Nash Equilibrium for Randomly Switching Differential–Algebraic Games." IEEE Transactions on Automatic Control 65, no. 8: 3286-3301.

Journal article
Published: 23 August 2019 in Electronics
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This paper presents an experimental demonstration of a novel real-time Energy Management System (EMS) for inverter-based microgrids to achieve optimal economic operation using a simple dynamic algorithm without offline optimization process requirements. The dynamic algorithm solves the economic dispatch problem offering an adequate stability performance and an optimal power reference tracking under sudden load and generation changes. Convergence, optimality and frequency regulation properties of the real-time EMS are shown, and the effectiveness and compatibility with inner and primary controllers are validated in experiments, showing better performance on optimal power tracking and frequency regulation than conventional droop control power sharing techniques.

ACS Style

Carlos A. Macana; Hemanshu R. Pota; Quanyan Zhu; Josep M. Guerrero; Juan C. Vasquez. Experiments on a Real-Time Energy Management System for Islanded Prosumer Microgrids. Electronics 2019, 8, 925 .

AMA Style

Carlos A. Macana, Hemanshu R. Pota, Quanyan Zhu, Josep M. Guerrero, Juan C. Vasquez. Experiments on a Real-Time Energy Management System for Islanded Prosumer Microgrids. Electronics. 2019; 8 (9):925.

Chicago/Turabian Style

Carlos A. Macana; Hemanshu R. Pota; Quanyan Zhu; Josep M. Guerrero; Juan C. Vasquez. 2019. "Experiments on a Real-Time Energy Management System for Islanded Prosumer Microgrids." Electronics 8, no. 9: 925.

Journal article
Published: 29 July 2019 in IEEE Transactions on Control of Network Systems
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Increasing connectivity of communication networks enables large-scale distributed processing over networks and improves the efficiency for information exchange. However, malware and virus can take advantage of the high connectivity to spread over the network and take control of devices and servers for illicit purposes. In this paper, we use an SIS epidemic model to capture the virus spreading process and develop a virus-resistant weight adaptation scheme to mitigate the spreading over the network. We propose a differential game framework to provide a theoretic underpinning for decentralized mitigation in which nodes of the network cannot fully coordinate, and each node determines its own control policy based on local interactions with neighboring nodes. We characterize and examine the structure of the Nash equilibrium, and discuss the inefficiency of the Nash equilibrium in terms of minimizing the total cost of the whole network. A mechanism design through a penalty scheme is proposed to reduce the inefficiency of the Nash equilibrium and allow the decentralized policy to achieve social welfare for the whole network. We corroborate our results using numerical experiments and show that virus-resistance can be achieved by a distributed weight adaptation scheme.

ACS Style

Yunhan Huang; Quanyan Zhu. A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks. IEEE Transactions on Control of Network Systems 2019, 7, 944 -955.

AMA Style

Yunhan Huang, Quanyan Zhu. A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks. IEEE Transactions on Control of Network Systems. 2019; 7 (2):944-955.

Chicago/Turabian Style

Yunhan Huang; Quanyan Zhu. 2019. "A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks." IEEE Transactions on Control of Network Systems 7, no. 2: 944-955.

Preprint
Published: 01 July 2019
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Plug-in Hybrid Electric Vehicles (PHEVs) are gaining popularity due to their economic efficiency as well as their contribution to green management. PHEVs allow the driver to use electric power exclusively for driving and then switch to gasoline as needed. The more gasoline a vehicle uses, the higher cost is required for the trip. However, a PHEV cannot last for a long period on stored electricity without being recharged. Thus, it needs frequent recharging compared to traditional gasoline-powered vehicles. Moreover, the battery recharging time is usually long, which leads to longer delays on a trip. Therefore, it is necessary to provide a flexible navigation management scheme along with an efficient recharging schedule, which allows the driver to choose an optimal route based on the fuel-cost and time-to-destination constraints. In this paper, we present a formal model to solve this PHEV navigation management problem. The model is solved to provide a driver with a comprehensive routing plan including the potential recharging and refueling points that satisfy the given requirements, particularly the maximum fuel cost and the maximum trip time. In addition, we propose a price-based navigation control technique to achieve better load balance for the traffic system. Evaluation results show that the proposed formal models can be solved efficiently even with large road networks.

ACS Style

Mohammad Ashiqur Rahman; Hasan Shahriar; Ehab Al-Shaer; Quanyan Zhu. A Formal Approach for Efficient Navigation Management of Hybrid Electric Vehicles on Long Trips. 2019, 1 .

AMA Style

Mohammad Ashiqur Rahman, Hasan Shahriar, Ehab Al-Shaer, Quanyan Zhu. A Formal Approach for Efficient Navigation Management of Hybrid Electric Vehicles on Long Trips. . 2019; ():1.

Chicago/Turabian Style

Mohammad Ashiqur Rahman; Hasan Shahriar; Ehab Al-Shaer; Quanyan Zhu. 2019. "A Formal Approach for Efficient Navigation Management of Hybrid Electric Vehicles on Long Trips." , no. : 1.

Preprint
Published: 09 April 2019
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In this paper, we obtain generic bounds on the variances of estimation and prediction errors in time series analysis via an information-theoretic approach. It is seen in general that the error bounds are determined by the conditional entropy of the data point to be estimated or predicted given the side information or past observations. Additionally, we discover that in order to achieve the prediction error bounds asymptotically, the necessary and sufficient condition is that the "innovation" is asymptotically white Gaussian. When restricted to Gaussian processes and 1-step prediction, our bounds are shown to reduce to the Kolmogorov-Szeg\"o formula and Wiener-Masani formula known from linear prediction theory.

ACS Style

Song Fang; Mikael Skoglund; Karl Henrik Johansson; Hideaki Ishii; Quanyan Zhu. Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective. 2019, 1 .

AMA Style

Song Fang, Mikael Skoglund, Karl Henrik Johansson, Hideaki Ishii, Quanyan Zhu. Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective. . 2019; ():1.

Chicago/Turabian Style

Song Fang; Mikael Skoglund; Karl Henrik Johansson; Hideaki Ishii; Quanyan Zhu. 2019. "Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective." , no. : 1.