Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors...
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Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors, considering exploration cost, target detection value, exploration ability and other factors. Then, aiming at the unfavorable environment, e.g., obstacles and enemy interference, the authors design a method to maintain the connectivity of sensor network, under the conditions of effective detection of the targets. Simulation result shows that the proposed deployment strategy can achieve the dynamic optimization deployment under complex conditions.
In this paper, a novel online Q-Iearning approach is proposed to solve the Infinite Horizon Linear Quadratic Regulator (IHLQR) problem for continuous-time (CT) linear time-invariant (LMI) systems. The proposed Q-Iearn...
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In this paper, a novel online Q-Iearning approach is proposed to solve the Infinite Horizon Linear Quadratic Regulator (IHLQR) problem for continuous-time (CT) linear time-invariant (LMI) systems. The proposed Q-Iearning algorithm employing off-policy reinforcement learning (RL) technology improves the exploration ability of Q-Iearning to the state space. During the learning process, the Q-Iearning algorithm can be implemented just using the data sets which just contains the information of the behavior policy and the corresponding system state, thus is data- driven. Moreover, the data sets can be used repeatedly, which is computationally efficient. A mild condition on probing noise is established to ensure the converge of the proposed Q-Iearning algorithm. Simulation results demonstrate the effectiveness of the developed algorithm.
In this paper, we consider the plan recognition problem in the real-time strategy game. A probabilistic plan recognition algorithm is proposed to predict the future goals and identify the temporal logic tasks of the n...
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In this paper, we consider the plan recognition problem in the real-time strategy game. A probabilistic plan recognition algorithm is proposed to predict the future goals and identify the temporal logic tasks of the non-cooperative agent based on the observations. In order to model the temporal logic tasks, the plan library is composed of the Finite Transition System and Nondeterministic B ¨uchi Automation. Specially, we provide a unified framework to combine the plan recognition and the planning, and propose the probability calculation algorithm to calculate the posterior probability distribution of the goals and tasks. Finally, we verify the effectiveness of the proposed algorithm by the compared simulations.
In this paper,the adaptive robust control(ARC) algorithm is proposed for tracking control of the electric cylinder *** to the analysis of the working principle of the electric cylinder servo system,the model of the ...
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In this paper,the adaptive robust control(ARC) algorithm is proposed for tracking control of the electric cylinder *** to the analysis of the working principle of the electric cylinder servo system,the model of the system is established on the modified Lugre friction model,and single observer is designed for calculating friction as *** the ARC controller and adaptive law are designed for on-line parameters estimation,frictional compensation and the suppression external *** the Lyapunov approach,the stability of this system can be obtained in this *** simulation result shows that ARC can suppresses external disturbances effectively and realize real-time online parameter estimation.
In this paper, we propose a distributed adaptive approach for tracking problem without using leader's velocity information, where agents are modeled by Euler-Lagrange equations. It is assumed that only a small fra...
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In this paper, we propose a distributed adaptive approach for tracking problem without using leader's velocity information, where agents are modeled by Euler-Lagrange equations. It is assumed that only a small fraction of agents within the leader's communication range are informed about the position of the leader. Without using the leader's velocity information, a connectivity-preserving adaptive controller is proposed to achieve tracking control on Lagrangian systems with the leader of constant velocity. Moreover, position and velocity consensus can be achieved asymptotically with the proposed control strategy. Numerical simulations are further provided to illustrate the theoretical results.
In this paper, the fixed-time stabilization of Takagi-Sugeno (T-S) fuzzy system with discrete time delays and external disturbances is investigated via sliding-mode control. Firstly, a suitable controller and sliding-...
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This paper considers the distributed quadratic stabilization problems of uncertain continuous-time linear multiagent systems with undirected communication topologies. It is assumed that the agents have identical nomin...
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This paper considers the distributed quadratic stabilization problems of uncertain continuous-time linear multiagent systems with undirected communication topologies. It is assumed that the agents have identical nominal dynamics while subject to different norm-bounded parameter uncertainties, leading to weakly heterogeneous multi-agent systems. A distributed controller is proposed, based on the relative states of neighboring agents and a subset of absolute states of the agents. It is shown that the distributed quadratic stabilization problem under such a controller is equivalent to the H∞ control problems of a set of decoupled linear systems having the same dimensions as a single agent. A two-step algorithm is presented to construct the distributed robust controller, which does not involve any conservatism and meanwhile decouples the feedback gain design from the communication topology. Furthermore, the distributed quadratic H∞ control problem of uncertain linear multi-agent systems with external disturbances is discussed, which can be reduced to the scaled H∞ control problems of a set of independent systems whose dimensions are equal to that of a single agent.
Graph neural networks(GNNs) have shown great popularity and achieved promising performance on various graph-based tasks in the past years. However, there is little work that explores the information fusion mechanism, ...
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Graph neural networks(GNNs) have shown great popularity and achieved promising performance on various graph-based tasks in the past years. However, there is little work that explores the information fusion mechanism, which plays an import role in GNNs. Besides, datasets in the real world often have noises, which make the information fusion difficult. In this paper, we give an information-theoretic explanation. Specifically, we focus on how the information from topological structures and node features fuses and how different information contributes to the downstream task. Furthermore, we propose a general framework named M-GCN to express the fusion process in GNNs. Graph embeddings and feature graph are introduced to extract the information from topological structure and node features separately in M-GCN. Extensive experiments are conducted on several benchmark datasets and experimental results show that our proposed models are more robust and outperform state-of-the-art methods.
In the field of radar data processing, track interruption seriously affects target tracking, track fusion, and other *** existing track segment association algorithms have low correlation accuracy in dense distributed...
In the field of radar data processing, track interruption seriously affects target tracking, track fusion, and other *** existing track segment association algorithms have low correlation accuracy in dense distributed or long-time interruption situations. To this purpose, a dense multi-target track segment association(DMTTSA) algorithm is proposed. Firstly, two identical networks based on the multi-head probability sparse(ProbSparse) self-attention are used to capture the long-term dependencies of the tracks. Then, the bidirectional quadruplet hard sample loss(BiQuaHard loss) is constructed to make the tracks belonging to the same targets closer and the tracks belonging to the different targets farther. Finally, DMTTSA takes the closest track pairs in the feature space as the associated tracks and divides the unassociated tracks into the birth and dead tracks in chronological order. Some comparative experiments are carried out to show the anti-noise performance of the DMTTSA, as well as the effectiveness of solving the problem of dense multi-target track interruption.
As a cutting-edge branch of unmanned aerial vehicle(UAV)technology,the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors,due to its remarkable merits in functionali...
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As a cutting-edge branch of unmanned aerial vehicle(UAV)technology,the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors,due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks,e.g.,search and rescue,fire-fighting,reconnaissance,and *** path planning(CPP)is a key problem for a UAV group in executing tasks *** this paper,an attempt is made to perform a comprehensive review of the research on CPP for UAV ***,a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements,i.e.,task,UAV group,and environment,as a basis for a comprehensive classification of different types of CPP *** following the proposed framework,a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified ***,a review and a statistical analysis are presented based on the taxonomy,emphasizing the coordinative elements in the existing CPP *** addition,a collection of challenging CPP problems are provided to highlight future research directions.
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