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This article is concerned with the robust adaptive control circuit design for pulse wide modulation (PWM)-based dc-dc buck converters with load variations and exogenous disturbances. A robust adaptive perturbation rejection control strategy is first developed to suppress time-varying and state-dependent perturbations, which are composed of load variations and disturbances. Then, equivalent analog control circuits of the adaptive control strategy are implemented on the basis of the circuit theory. Bounded tracking of the closed-loop converter system in the presence of perturbations is achieved based on the Lyapunov stability theorem. Simulations and experimental results are provided to validate the efficiency of the proposed adaptive perturbation rejection control strategy in a dc-dc buck converter system.
Xiaozheng Jin; Jiahu Qin; Wei Xing Zheng; Chengwei Yang. Adaptive Perturbation Rejection Control for a Class of Converter Systems With Circuit Realization. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021, PP, 1 -11.
AMA StyleXiaozheng Jin, Jiahu Qin, Wei Xing Zheng, Chengwei Yang. Adaptive Perturbation Rejection Control for a Class of Converter Systems With Circuit Realization. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021; PP (99):1-11.
Chicago/Turabian StyleXiaozheng Jin; Jiahu Qin; Wei Xing Zheng; Chengwei Yang. 2021. "Adaptive Perturbation Rejection Control for a Class of Converter Systems With Circuit Realization." IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, no. 99: 1-11.
In this technical note, the reachability analysis is investigated for the linear discrete-time systems in the presence of malicious cyber attacks. Moreover, the system disturbance and measurement noise are assumed to be unknown-but-bounded (UBB). Then, the attack model is built and a novel attack detector which depends on the estimate residue is developed. Based on the stealthy cyber attack set, reachability analysis is done and the corresponding reachable set is computed. In order to reduce the computation burden, a recursive reachable set computation algorithm is proposed. Finally, an example is provided to illustrate the vadility of main results.
Hao Liu; Ben Niu; Jiahu Qin. Reachability Analysis for Linear Discrete-Time Systems Under Stealthy Cyber Attacks. IEEE Transactions on Automatic Control 2021, 66, 4444 -4451.
AMA StyleHao Liu, Ben Niu, Jiahu Qin. Reachability Analysis for Linear Discrete-Time Systems Under Stealthy Cyber Attacks. IEEE Transactions on Automatic Control. 2021; 66 (9):4444-4451.
Chicago/Turabian StyleHao Liu; Ben Niu; Jiahu Qin. 2021. "Reachability Analysis for Linear Discrete-Time Systems Under Stealthy Cyber Attacks." IEEE Transactions on Automatic Control 66, no. 9: 4444-4451.
In this article, we study a multiplayer Stackelberg-Nash game (SNG) pertaining to a nonlinear dynamical system, including one leader and multiple followers. At the higher level, the leader makes its decision preferentially with consideration of the reaction functions of all followers, while, at the lower level, each of the followers reacts optimally to the leader's strategy simultaneously by playing a Nash game. First, the optimal strategies for the leader and the followers are derived from down to the top, and these strategies are further shown to constitute the Stackelberg-Nash equilibrium points. Subsequently, to overcome the difficulty in calculating the equilibrium points analytically, we develop a novel two-level value iteration-based integral reinforcement learning (VI-IRL) algorithm that relies only upon partial information of system dynamics. We establish that the proposed method converges asymptotically to the equilibrium strategies under the weak coupling conditions. Moreover, we introduce effective termination criteria to guarantee the admissibility of the policy (strategy) profile obtained from a finite number of iterations of the proposed algorithm. In the implementation of our scheme, we employ neural networks (NNs) to approximate the value functions and invoke the least-squares methods to update the involved weights. Finally, the effectiveness of the developed algorithm is verified by two simulation examples.
Man Li; Jiahu Qin; Nikolaos M. Freris; Daniel W. C. Ho. Multiplayer Stackelberg-Nash Game for Nonlinear System via Value Iteration-Based Integral Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems 2020, PP, 1 -12.
AMA StyleMan Li, Jiahu Qin, Nikolaos M. Freris, Daniel W. C. Ho. Multiplayer Stackelberg-Nash Game for Nonlinear System via Value Iteration-Based Integral Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems. 2020; PP (99):1-12.
Chicago/Turabian StyleMan Li; Jiahu Qin; Nikolaos M. Freris; Daniel W. C. Ho. 2020. "Multiplayer Stackelberg-Nash Game for Nonlinear System via Value Iteration-Based Integral Reinforcement Learning." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-12.
This paper addresses the robust trajectory tracking control problem for a class of wheeled robotic systems with perturbations caused by measurement errors, internal uncertainties, and exogenous disturbances. An adaptive technique is utilized to estimate the effects of perturbations. Then, on the basis of the adaptive estimations, perturbation rejection control schemes are developed to construct the kinematic control and dynamic control strategies. By utilizing Lyapunov stability theory, bounded tracking of the desired trajectory and asymptotic tracking of auxiliary azimuthal angular velocity and forward speed of the robot can be achieved respectively in the fact of perturbations. Furthermore, the adaptive perturbation rejection control (APRC) strategies are implemented physically by analog circuits to generate driving voltages of DC motors in the robot reality. The efficiency of the proposed trajectory tracking control method is validated by a robotic system.
Xiaozheng Jin; Jizhou Yu; Jiahu Qin; Wei Xing Zheng; Jing Chi. Adaptive perturbation rejection control and driving voltage circuit designs of wheeled mobile robots. Journal of the Franklin Institute 2020, 358, 1185 -1213.
AMA StyleXiaozheng Jin, Jizhou Yu, Jiahu Qin, Wei Xing Zheng, Jing Chi. Adaptive perturbation rejection control and driving voltage circuit designs of wheeled mobile robots. Journal of the Franklin Institute. 2020; 358 (2):1185-1213.
Chicago/Turabian StyleXiaozheng Jin; Jizhou Yu; Jiahu Qin; Wei Xing Zheng; Jing Chi. 2020. "Adaptive perturbation rejection control and driving voltage circuit designs of wheeled mobile robots." Journal of the Franklin Institute 358, no. 2: 1185-1213.
Inspired by the collective decision making in biological systems, such as honeybee swarm searching for a new colony, we study a dynamic collective choice problem for large-population systems with the purpose of realizing certain advantageous features observed in biology. This problem focuses on the situation where a large number of heterogeneous agents subject to adversarial disturbances move from initial positions toward one of the destinations in a finite time while trying to remain close to the average trajectory of all agents. To overcome the complexity of this problem resulting from the large population and the heterogeneity of agents, and also to enforce some specific choices by individuals, we formulate the problem under consideration as a robust mean-field game with non-convex and non-smooth cost functions. Through Nash equivalence principle, we first deal with a single-player H∞ tracking problem by taking the population behavior as a fixed trajectory, and then establish a mean-field system to estimate the population behavior. Optimal control strategies and worst disturbances, independent of the population size, are designed, which give a way to realize the collective decision-making behavior emerged in biological systems. We further prove that the designed strategies constitute εN-Nash equilibrium, where εN goes toward zero as the number of agents increases to infinity. The effectiveness of the proposed results are illustrated through two simulation examples.
Man Li; Jiahu Qin; YaoNan Wang; Yu Kang. Bio-Inspired Dynamic Collective Choice in Large-Population Systems: A Robust Mean-Field Game Perspective. IEEE Transactions on Neural Networks and Learning Systems 2020, 1 -11.
AMA StyleMan Li, Jiahu Qin, YaoNan Wang, Yu Kang. Bio-Inspired Dynamic Collective Choice in Large-Population Systems: A Robust Mean-Field Game Perspective. IEEE Transactions on Neural Networks and Learning Systems. 2020; (99):1-11.
Chicago/Turabian StyleMan Li; Jiahu Qin; YaoNan Wang; Yu Kang. 2020. "Bio-Inspired Dynamic Collective Choice in Large-Population Systems: A Robust Mean-Field Game Perspective." IEEE Transactions on Neural Networks and Learning Systems , no. 99: 1-11.
This paper is concerned with developing a novel distributed Kalman filtering algorithm over wireless sensor networks based on randomized consensus strategy. Compared with centralized algorithm, distributed filtering techniques require less computation per sensor and lead to more robust estimation since they simply use the information from the neighboring nodes in the network. However, poor local sensor estimation caused by limited observability and network topology changes which interfere the global consensus are challenging issues. Motivated by this observation, we propose a novel randomized gossip based distributed Kalman filtering algorithm. Information exchange and computation in the proposed algorithm can be carried out in an arbitrarily connected network of nodes. In addition, the computational burden can be distributed for a sensor which communicates with a stochastically selected neighbor at each clock step under schemes of gossip algorithm. In this case, the error covariance matrix changes stochastically at every clock step, thus the convergence is considered in a probabilistic sense. We provide the mean square convergence analysis of the proposed algorit
Jiahu Qin; Jie Wang; Ling Shi; Yu Kang. Randomized Consensus-Based Distributed Kalman Filtering Over Wireless Sensor Networks. IEEE Transactions on Automatic Control 2020, 66, 3794 -3801.
AMA StyleJiahu Qin, Jie Wang, Ling Shi, Yu Kang. Randomized Consensus-Based Distributed Kalman Filtering Over Wireless Sensor Networks. IEEE Transactions on Automatic Control. 2020; 66 (8):3794-3801.
Chicago/Turabian StyleJiahu Qin; Jie Wang; Ling Shi; Yu Kang. 2020. "Randomized Consensus-Based Distributed Kalman Filtering Over Wireless Sensor Networks." IEEE Transactions on Automatic Control 66, no. 8: 3794-3801.
This paper investigates the charging problem of plug-in electric vehicles (PEVs) in a smart charging station (SCS) under a new interaction mechanism that allows the interactions among PEVs. The target is to coordinate the charging strategies of all PEVs such that the energy cost of SCS is minimized without compromising a set of constraints for PEVs and SCS. To this end, we first construct a non-cooperative game framework, in which each player (i.e., PEV) expects to minimize its cost by choosing the optimal charging strategy over the entire charging horizon. Then, the existence and optimality of Nash equilibrium (NE) for the formulated non-cooperative game is provided. Moreover, to find the unique generalized Nash equilibrium (GNE), we propose a distributed GNE-seeking algorithm based on the Newton fixed-point method. And a fast alternating direction multiplier method (fast-ADMM) framework is applied to determine the best response of PEVs. The convergence of the proposed distributed GNE-seeking algorithm and PEVs’ best response are also provided with theoretical analysis. Simulations are presented at last to validate the effectiveness of the proposed algorithm.
Yanni Wan; Jiahu Qin; Fangyuan Li; Xinghuo Yu; Yu Kang. Game Theoretic-Based Distributed Charging Strategy for PEVs in a Smart Charging Station. IEEE Transactions on Smart Grid 2020, 12, 538 -547.
AMA StyleYanni Wan, Jiahu Qin, Fangyuan Li, Xinghuo Yu, Yu Kang. Game Theoretic-Based Distributed Charging Strategy for PEVs in a Smart Charging Station. IEEE Transactions on Smart Grid. 2020; 12 (1):538-547.
Chicago/Turabian StyleYanni Wan; Jiahu Qin; Fangyuan Li; Xinghuo Yu; Yu Kang. 2020. "Game Theoretic-Based Distributed Charging Strategy for PEVs in a Smart Charging Station." IEEE Transactions on Smart Grid 12, no. 1: 538-547.
This paper investigates the circular motion of a group of n(n ≥ 2) nonholonomic robots over time-varying communication networks. We aim to achieve a balanced circular motion centered at a location determined by each robot under the assumption that the global ranking of any robot is unknown. A back-stepping based controller is firstly designed to make all the robots rotate around a common center, the position of which is obtained through executing a consensus algorithm by each robot. Then, the maximum and minimum consensus algorithm and a distributed modified ordinal ranking algorithm are applied to set the rotation radius, angular velocity, and orientation parameters in a distributed manner such that all the robots can uniformly space on the common circle. At last, the effectiveness of the proposed algorithms is illustrated through a simulation example.
Guoyong Chen; Weiming Fu; Yu Kang; Jiahu Qin; Wei Xing Zheng. Circular motion of multiple nonholonomic robots under switching topology with ordinal ranking. Journal of the Franklin Institute 2020, 357, 10737 -10756.
AMA StyleGuoyong Chen, Weiming Fu, Yu Kang, Jiahu Qin, Wei Xing Zheng. Circular motion of multiple nonholonomic robots under switching topology with ordinal ranking. Journal of the Franklin Institute. 2020; 357 (15):10737-10756.
Chicago/Turabian StyleGuoyong Chen; Weiming Fu; Yu Kang; Jiahu Qin; Wei Xing Zheng. 2020. "Circular motion of multiple nonholonomic robots under switching topology with ordinal ranking." Journal of the Franklin Institute 357, no. 15: 10737-10756.
This article considers distributed optimization problems of complex cyber‐physical networks, whose goal is to minimize a global function consisting of a sum of local functions possessed by each node, when the communication network is suffering L ‐local deception attacks. After showing that merely 1‐local deception attacks can arbitrarily affect the outcome of any distributed optimization algorithms without being detected, we propose a resilient consensus‐based distributed optimization algorithm, where the estimation for the optimizer of each node is updated according to its subgradient and its partial neighbors' estimation. Then, we provide the conditions for the proposed algorithm to ensure that all the nodes can make an agreement and converge to the convex hull of the local optimizer of their functions in the presence of L ‐local deception attacks. Finally, some simulation examples are presented to demonstrate the effectiveness of the proposed algorithm.
Weiming Fu; Qichao Ma; Jiahu Qin; Yu Kang. Resilient consensus‐based distributed optimization under deception attacks. International Journal of Robust and Nonlinear Control 2020, 31, 1803 -1816.
AMA StyleWeiming Fu, Qichao Ma, Jiahu Qin, Yu Kang. Resilient consensus‐based distributed optimization under deception attacks. International Journal of Robust and Nonlinear Control. 2020; 31 (6):1803-1816.
Chicago/Turabian StyleWeiming Fu; Qichao Ma; Jiahu Qin; Yu Kang. 2020. "Resilient consensus‐based distributed optimization under deception attacks." International Journal of Robust and Nonlinear Control 31, no. 6: 1803-1816.
We consider a scenario in which a DoS attacker with the limited power resource and the purpose of degrading the system performance, jams a wireless network through which the packet from a sensor is sent to a remote estimator. To degrade the estimation quality most effectively with a given energy budget, the attacker aims to solve the problem of how much power to obstruct the channel each time, which is the recently proposed optimal attack energy management problem. The existing works are built on an ideal network model in which the packet dropout never occurs when the attack is absent. To encompass wireless transmission losses, we introduce the signal-to-interference-plus-noise ratio-based network. First we focus on the case when the attacker employs the constant power level. To maximize the expected terminal estimation error at the remote estimator, we provide some more relaxed sufficient conditions compared with the existing work for the existence of an explicit solution to the optimal static attack energy management problem and the solution is constructed. For the other important index of system performance, the average expected estimation error, the associated sufficient conditions are also derived based on a different analysis approach with the existing work. And a feasible method is presented for both indexes to seek the optimal constant attack power level when the system fails to meet the proposed sufficient conditions. Then when the real-time ACK information can be acquired, a Markov decision process (MDP) based algorithm is designed to solve the optimal dynamic attack energy management problem. We further study the optimal tradeoff between attack energy and system degradation. Specifically, by moving the energy constraint into the objective function to maximize the system index and minimize the energy consumption simultaneously, the other MDP based algorithm is proposed to find the optimal dynamic attack power policy which is further shown to have a monotone structure. The theoretical results are illustrated by simulations.
Jiahu Qin; Menglin Li; Jie Wang; Ling Shi; Yu Kang; Wei Xing Zheng. Optimal Denial-of-Service attack energy management against state estimation over an SINR-based network. Automatica 2020, 119, 109090 .
AMA StyleJiahu Qin, Menglin Li, Jie Wang, Ling Shi, Yu Kang, Wei Xing Zheng. Optimal Denial-of-Service attack energy management against state estimation over an SINR-based network. Automatica. 2020; 119 ():109090.
Chicago/Turabian StyleJiahu Qin; Menglin Li; Jie Wang; Ling Shi; Yu Kang; Wei Xing Zheng. 2020. "Optimal Denial-of-Service attack energy management against state estimation over an SINR-based network." Automatica 119, no. : 109090.
This article studies the containment control problem for a group of linear systems, consisting of more than one leader, over switching topologies. The input matrices of these linear systems are not required to have full-row rank and the switching can be arbitrary, making the problem quite general and challenging. We propose a novel analysis framework from the viewpoint of a state transition matrix. Specifically, according to the inherent linearity, we successfully establish a connection between state transition matrices of the above multileader system and a virtual leader-following system obtained by combining those leaders. This enlightening result relates the containment problem to a consensus one. Then, by analyzing the property of the state transition matrix, we uncover that each component of any follower's state converges to the convex hull spanned by the corresponding components of the leaders', provided some mild conditions are satisfied. These conditions are derived in terms of the concept of a positive linear system. A special case of the second-order linear system is further discussed to illustrate these conditions. Moreover, two different design methods of the feedback gain matrix are provided, which additionally require that the network topology contains a united spanning tree all the time.
Cong Zhang; Jiahu Qin; Qichao Ma; Yang Shi; Yu Kang. On Containment for Linear Systems With Switching Topologies: A Novel State Transition Matrix Perspective. IEEE Transactions on Cybernetics 2020, 1 -12.
AMA StyleCong Zhang, Jiahu Qin, Qichao Ma, Yang Shi, Yu Kang. On Containment for Linear Systems With Switching Topologies: A Novel State Transition Matrix Perspective. IEEE Transactions on Cybernetics. 2020; ():1-12.
Chicago/Turabian StyleCong Zhang; Jiahu Qin; Qichao Ma; Yang Shi; Yu Kang. 2020. "On Containment for Linear Systems With Switching Topologies: A Novel State Transition Matrix Perspective." IEEE Transactions on Cybernetics , no. : 1-12.
In this article, the consensus problem of linear systems is revisited from a novel geometric perspective. The interaction network of these systems is assumed to be piecewise fixed. Moreover, it is allowed to be disconnected at any time but holds a quite mild joint connectivity property. The system matrix is marginally stable and the input matrix is not of full-row rank. By directly examining the subspace determined by the network, we first establish convergence by resorting to an observability condition. Then, according to joint connectivity, we are able to extend this convergence uniformly to the entire orthogonal complement of the consensus manifold. In this way, we work out the necessary and sufficient condition for exponential consensus. It turns out that, with a suitably designed feedback matrix, exponential consensus can be realized globally and uniformly if and only if a jointly (δ,T)-connected condition and an observability condition relying only on the system and input matrices are satisfied. We also characterize the lower bound of the convergence rate. Simple yet effective examples are presented to illustrate the findings.
Qichao Ma; Jiahu Qin; Wei Xing Zheng; Yang Shi; Yu Kang. Exponential Consensus of Linear Systems Over Switching Network: A Subspace Method to Establish Necessity and Sufficiency. IEEE Transactions on Cybernetics 2020, 1 -10.
AMA StyleQichao Ma, Jiahu Qin, Wei Xing Zheng, Yang Shi, Yu Kang. Exponential Consensus of Linear Systems Over Switching Network: A Subspace Method to Establish Necessity and Sufficiency. IEEE Transactions on Cybernetics. 2020; (99):1-10.
Chicago/Turabian StyleQichao Ma; Jiahu Qin; Wei Xing Zheng; Yang Shi; Yu Kang. 2020. "Exponential Consensus of Linear Systems Over Switching Network: A Subspace Method to Establish Necessity and Sufficiency." IEEE Transactions on Cybernetics , no. 99: 1-10.
Distributed data clustering in sensor networks is receiving increasing attention with the development of network technology. A variety of algorithms for distributed data clustering have been proposed recently. However, most of these algorithms have trouble with either non-Gaussian shaped data clustering or model order selection problem. In order to address both of these problems simultaneously, we propose a novel discriminative clustering algorithm via normalized information measures and then extend it to a distributed one by borrowing consensus algorithms from multi-agent consensus community. More specifically, we first select the normalized information distance (NID) between cluster data and cluster labels as the objective function, by minimizing which, a Minimum Normalized Information Distancebased (MNID) algorithm with capabilities of non-Gaussian data clustering and model selection is then proposed. Next, to further implement the MNID algorithm in a distributed manner, we employ some finite-time multi-agent consensus algorithms over the sensor networks to calculate the global model parameters, where only local intermediate variables are exchanged between one-hop neighbors. In this way, the data privacy can be preserved. Finally, the validity of the proposed algorithms is demonstrated through numerical tests on both synthetic and real data. It is shown that the proposed algorithms outperform some existing clustering algorithms on both Gaussian and non-Gaussian data.
Jiahu Qin; Yingda Zhu; Weiming Fu. Distributed Clustering Algorithm in Sensor Networks via Normalized Information Measures. IEEE Transactions on Signal Processing 2020, 68, 3266 -3279.
AMA StyleJiahu Qin, Yingda Zhu, Weiming Fu. Distributed Clustering Algorithm in Sensor Networks via Normalized Information Measures. IEEE Transactions on Signal Processing. 2020; 68 (99):3266-3279.
Chicago/Turabian StyleJiahu Qin; Yingda Zhu; Weiming Fu. 2020. "Distributed Clustering Algorithm in Sensor Networks via Normalized Information Measures." IEEE Transactions on Signal Processing 68, no. 99: 3266-3279.
The microgrid is widely recognized as a promising concept for integrating distributed energy resources (DERs). Considering the enormous number of DERs, the future smart grid will likely be a grid containing a number of interconnected microgrids. Thus, the optimal power flow (OPF) problem should be studied by properly taking account of the coupling between the microgrids. In this paper, the models of standalone microgrid and coupled microgrids are formulated first. Then, a decentralized approach is shown in detail to cooperatively solve the coupled OPF problem. Specifically, each microgrid solves the local OPF problem, leading to the optimal solution of the coupled OPF problem via negotiation between microgrids. The privacy of each microgrid is also preserved since no sensitive information is shared between microgrids. Finally, simulation results show that the optimality gaps between the proposed approach and an existing solver are small for both the AC OPF and the DC OPF. Furthermore, the total cost of the microgrids is reduced by the cooperative OPF.
Fangyuan Li; Jiahu Qin; Yanni Wan; Tao Yang. Decentralized Cooperative Optimal Power Flow of Multiple Interconnected Microgrids via Negotiation. IEEE Transactions on Smart Grid 2020, 11, 3827 -3836.
AMA StyleFangyuan Li, Jiahu Qin, Yanni Wan, Tao Yang. Decentralized Cooperative Optimal Power Flow of Multiple Interconnected Microgrids via Negotiation. IEEE Transactions on Smart Grid. 2020; 11 (5):3827-3836.
Chicago/Turabian StyleFangyuan Li; Jiahu Qin; Yanni Wan; Tao Yang. 2020. "Decentralized Cooperative Optimal Power Flow of Multiple Interconnected Microgrids via Negotiation." IEEE Transactions on Smart Grid 11, no. 5: 3827-3836.
The resilient cooperative source seeking problem of double-integrator multi-robot systems under d-local deception attacks is investigated. We first propose a resilient cooperative control algorithm and present a necessary and sufficient condition for its achievement of resilient consensus. Then, through applying the particle swarm optimization algorithm to the resilient cooperative control algorithm, a resilient cooperative source seeking algorithm is proposed. We show that this algorithm can solve the source seeking problems under d-local deception attacks with an additional potential of addressing the multiple local extrema case. Finally, the feasibility of the proposed algorithms is illustrated by simulation and experimental results.
Weiming Fu; Jiahu Qin; Wei Xing Zheng; Yuhang Chen; Yu Kang. Resilient Cooperative Source Seeking of Double-Integrator Multi-Robot Systems Under Deception Attacks. IEEE Transactions on Industrial Electronics 2020, 68, 4218 -4227.
AMA StyleWeiming Fu, Jiahu Qin, Wei Xing Zheng, Yuhang Chen, Yu Kang. Resilient Cooperative Source Seeking of Double-Integrator Multi-Robot Systems Under Deception Attacks. IEEE Transactions on Industrial Electronics. 2020; 68 (5):4218-4227.
Chicago/Turabian StyleWeiming Fu; Jiahu Qin; Wei Xing Zheng; Yuhang Chen; Yu Kang. 2020. "Resilient Cooperative Source Seeking of Double-Integrator Multi-Robot Systems Under Deception Attacks." IEEE Transactions on Industrial Electronics 68, no. 5: 4218-4227.
In this paper, we aim to investigate the synchronization problem of dynamical systems, which can be of generic linear or Lipschitz nonlinear type, communicating over directed switching network topologies. A mild connectivity assumption on the switching topologies is imposed, which allows them to be directed and jointly connected. We propose a novel analysis framework from both algebraic and geometric perspectives to justify the attractiveness of the synchronization manifold. Specifically, it is proven that the complementary space of the synchronization manifold can be spanned by certain subspaces constructed from the network structure. This allows to project the states of the dynamical systems onto these subspaces and transform the synchronization problem under consideration equivalently into a convergence one of the projected states in each subspace. Then, assuming the joint connectivity condition on the communication topologies, we are able to work out a unified convergence analysis for both types of dynamical systems. More specifically, for partialstate coupled generic linear systems, it is proven that synchronization can be reached if an extra condition, which is easy to verify in several cases, on the system dynamics is satisfied. For Lipschitz nonlinear systems with positive-definite inner coupling matrix, synchronization is realized if the coupling strength is strong enough to stabilize the evolution of the projected states in each subspace under certain conditions. The above claims generalize the existing results concerning both types of dynamical systems to so far the most general framework. Some illustrative examples are provided to verify our theoretical findings.
Jiahu Qin; Qichao Ma; Xinghuo Yu; Long Wang. On Synchronization of Dynamical Systems Over Directed Switching Topologies: An Algebraic and Geometric Perspective. IEEE Transactions on Automatic Control 2020, 65, 5083 -5098.
AMA StyleJiahu Qin, Qichao Ma, Xinghuo Yu, Long Wang. On Synchronization of Dynamical Systems Over Directed Switching Topologies: An Algebraic and Geometric Perspective. IEEE Transactions on Automatic Control. 2020; 65 (12):5083-5098.
Chicago/Turabian StyleJiahu Qin; Qichao Ma; Xinghuo Yu; Long Wang. 2020. "On Synchronization of Dynamical Systems Over Directed Switching Topologies: An Algebraic and Geometric Perspective." IEEE Transactions on Automatic Control 65, no. 12: 5083-5098.
In this paper, the time-varying group formation control for linear multi-agent systems under directed communication topology is investigated from an observer viewpoint. Different from the existing works on the time-varying group formation, the groups herein could have a cyclic partition, which is more common in real applications than the topology with the acyclic groups. The leaderless time-varying group formation problem is studied first. An observer-based distributed protocol is presented for each agent, where the observer is used to estimate the unmeasurable state utilizing the output information. Then, to broaden the scope of applications, we further study the leader-following case, in which there exists a leader with nonzero and bounded input for each subgroup. To tackle this problem, we take the input of the leader as a disturbance, and develop the new forms of control protocols with nonlinear functions. Furthermore, for both cases, it is shown that under the formation feasible conditions, the desired time-varying group formation can be achieved if the strong enough intra-group coupling is selected and the corresponding digraph of each subgraph contains a directed spanning tree. Finally, two simulation examples are given.
Man Li; Qichao Ma; Chongjian Zhou; Jiahu Qin; Yu Kang. Distributed time-varying group formation control for generic linear systems with observer-based protocols. Neurocomputing 2020, 397, 244 -252.
AMA StyleMan Li, Qichao Ma, Chongjian Zhou, Jiahu Qin, Yu Kang. Distributed time-varying group formation control for generic linear systems with observer-based protocols. Neurocomputing. 2020; 397 ():244-252.
Chicago/Turabian StyleMan Li; Qichao Ma; Chongjian Zhou; Jiahu Qin; Yu Kang. 2020. "Distributed time-varying group formation control for generic linear systems with observer-based protocols." Neurocomputing 397, no. : 244-252.
In this paper, we study the problem of finding the least square solutions of over-determined linear algebraic equations over networks in a distributed manner. Each node has access to one of the linear equations and holds a dynamic state. We first propose a distributed least square solver over connected undirected interaction graphs and establish a necessary and sufficient on the step-size under which the algorithm exponentially converges to the least square solution. Next, we develop a distributed least square solver over strongly connected directed graphs and show that the proposed algorithm exponentially converges to the least square solution provided the step-size is sufficiently small. Moreover, we develop a finite-time least square solver by equipping the proposed algorithms with a finite-time decentralized computation mechanism. The theoretical findings are validated and illustrated by numerical simulation examples.
Tao Yang; Jemin George; Jiahu Qin; Xinlei Yi; Junfeng Wu. Distributed least squares solver for network linear equations. Automatica 2020, 113, 108798 .
AMA StyleTao Yang, Jemin George, Jiahu Qin, Xinlei Yi, Junfeng Wu. Distributed least squares solver for network linear equations. Automatica. 2020; 113 ():108798.
Chicago/Turabian StyleTao Yang; Jemin George; Jiahu Qin; Xinlei Yi; Junfeng Wu. 2020. "Distributed least squares solver for network linear equations." Automatica 113, no. : 108798.
In many areas such as sensor networks and smart grid, synchronized monitoring and control are usually required to enable the functionalities of the overall system. Clock synchronization is treated as a precondition to satisfy the synchronized monitoring and control. However, clock synchronization alone is not sufficient to meet these requirements, especially for the situations where low cost distributed embedded systems are involved. Latency and hardware resources are typical impact factors to the synchronization. This paper focuses on proposing a task alignment framework for low cost distributed systems relying only on a minimal set of hardware components. The system architecture and hardware components are presented first. Then the paper presents a task alignment algorithm under the proposed framework. The issues such as inevitable communication delays and execution delays are also addressed. Finally, simulation results verify the effectiveness of the proposed framework.
Fangyuan Li; Yanni Wan; Jiahu Qin. A Task Alignment Framework for Low Cost Distributed Systems targeting Synchronized Monitoring and Control. IFAC-PapersOnLine 2019, 52, 340 -345.
AMA StyleFangyuan Li, Yanni Wan, Jiahu Qin. A Task Alignment Framework for Low Cost Distributed Systems targeting Synchronized Monitoring and Control. IFAC-PapersOnLine. 2019; 52 (24):340-345.
Chicago/Turabian StyleFangyuan Li; Yanni Wan; Jiahu Qin. 2019. "A Task Alignment Framework for Low Cost Distributed Systems targeting Synchronized Monitoring and Control." IFAC-PapersOnLine 52, no. 24: 340-345.
This paper considers the interval consensus problems of discrete-time multi-agent systems over random interaction networks, where each agent can impose a lower and an upper bound, i.e., a local constraint interval, on the achievable consensus value. We show that if the intersection of the intervals is nonempty, it holds as a sure event that the states of all the agents converge to a common value inside that intersection, i.e., the interval consensus can be achieved almost surely. Convergence analysis is performed through developing a robust consensus analysis of random networks in view of a martingale convergence lemma. Numerical examples are also exhibited to verify the validity of the theoretical results.
Weiming Fu; Jiahu Qin; Junfeng Wu; Wei Xing Zheng; Yu Kang. Interval consensus over random networks. Automatica 2019, 111, 108603 .
AMA StyleWeiming Fu, Jiahu Qin, Junfeng Wu, Wei Xing Zheng, Yu Kang. Interval consensus over random networks. Automatica. 2019; 111 ():108603.
Chicago/Turabian StyleWeiming Fu; Jiahu Qin; Junfeng Wu; Wei Xing Zheng; Yu Kang. 2019. "Interval consensus over random networks." Automatica 111, no. : 108603.