Traditional task allocation methods for unmanned swarm systems ignore the effects of actual paths, resulting in estimation accuracy reduction. This paper formulates task planning problem by incorporating physical and ...
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ISBN:
(数字)9798350384185
ISBN:
(纸本)9798350384192
Traditional task allocation methods for unmanned swarm systems ignore the effects of actual paths, resulting in estimation accuracy reduction. This paper formulates task planning problem by incorporating physical and logical constraints, and establishes an integrated framework of task allocation and path planning. Conflict-based search method is used to address path planning with physical constraints. A genetic algorithm is employed to solve multi-traveling salesman allocation problem. A bounded suboptimal optimization, a data dictionary, and an island model are introduced to accelerate the convergence speed of the genetic algorithm. The experiments verify that compared to the decoupled task planning methods, the proposed method improves task execution efficiency and remains adaptability to various complex spatial maps.
Based on the background of photovoltaic development in the whole county and the demand for energy storage on the user-side, this paper establishes an economic evaluation model of user-side photovoltaic energy storage ...
Based on the background of photovoltaic development in the whole county and the demand for energy storage on the user-side, this paper establishes an economic evaluation model of user-side photovoltaic energy storage system considering shared energy storage. Firstly, three schemes of no energy storage, independent energy storage and shared energy storage are proposed, and the influence of photovoltaic energy storage system loss on annual power generation is considered. Secondly, based on the whole life cycle theory, the cost and benefit model of the user-side photovoltaic energy storage system is established, and the unit electricity cost of the user is used as the evaluation index. Finally, the relevant data of user in Hubei area are analyzed as an example to verify the effectiveness of the model and provide reference for the investment of related projects.
This paper addresses a distributed filtering problem for saturated systems over sensor network with measurement outliers, where the multiplicative noises are used to characterize the internal behaviors of system model...
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This paper addresses a distributed filtering problem for saturated systems over sensor network with measurement outliers, where the multiplicative noises are used to characterize the internal behaviors of system model. A χ detection is first constructed in the distributed filter to detect and remove the outliers existing in the measurement outputs caused by some external environment disturbances or malicious attacks. Moreover, the innovation sequence is applied to design filter as a compensation term. The objective of this paper is to find a locally optimal distributed filter such that, for both the measurement outliers and multiplicative noises, an upper bound of filtering error covariance is given and then a proper filter gain is derived to minimize the obtained upper bound matrix with the help of the matrix analysis theory and the probability knowledge. Finally, a numerical experiment is proposed to verify the validity of the designed distributed filtering algorithm.
the DC/AC interaction and the presence of the high nonlinearity in the HVDC links, these problems needs to examine the behavior of the system in steady-state and transient conditions in order to define the great role ...
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ISBN:
(数字)9798350346336
ISBN:
(纸本)9798350346343
the DC/AC interaction and the presence of the high nonlinearity in the HVDC links, these problems needs to examine the behavior of the system in steady-state and transient conditions in order to define the great role of protection functions and control parameters. Furthermore, control and protection systems are a vital part of an HVDC system to guarantee the safety, reliability, and stability of its operation. This research aim is to provide the solutions for control and protection problems that can occur in HVDC link. In this paper, we study how we mitigate this serious malfunction of commutation failure process in the operation of HVDC converters. Indeed some protection functions like the commutation failure prevention. Furthermore, HVDC transmission systems are vulnerable to DC faults and its protection becomes ever more important with the fast growth in the number of installations. Finally, the dynamics of the studied power system including the HVDC link is thoroughly investigated through various simulation scenarios.
This paper studies the algorithm design of variance-constrained $H_{\infty}$ state estimation problem for delayed memristive neural networks with adaptive event-triggered mechanism. The denial-of-service attacks are m...
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ISBN:
(纸本)9781665456579
This paper studies the algorithm design of variance-constrained $H_{\infty}$ state estimation problem for delayed memristive neural networks with adaptive event-triggered mechanism. The denial-of-service attacks are modeled by a series of random variables obeying the Bernoulli distribution with known probability. In addition, the adaptive event-triggered mechanism is introduced into the sensor-to-estimator to avoid unnecessary resource consumption. Our purpose is to construct a finite-horizon state estimation algorithm, and sufficient condition is obtained for the estimation error system satisfying the $H_{\infty}$ performance requirement and the error variance boundedness. Finally, a numerical example is used to illustrate the feasibility of the presented $H_{\infty}$ state estimation algorithm.
Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-g...
Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.
In this article, we study the formation problem for a group of mobile agents in a plane, in which the agents are required to maintain a distribution pattern, as well as to rotate around or remain static relative to a ...
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In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronizatio...
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ISBN:
(数字)9783907144107
ISBN:
(纸本)9798331540920
In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronization synthesis problem is formulated and thoroughly investigated, with the goal of characterizing the allowable heterogeneity among the agents to ensure synchronization under a uniform controller. The solvability condition is provided in terms of the phases of the residue matrices of the agents at the persistent modes. When the condition is satisfied, a design procedure is given, producing a low-gain synchronizing controller. Numerical examples are given to illustrate the results.
Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with ...
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In this paper, we investigate the simultaneous state and unknown input (SUI) filtering issue for a class of multi-sensor networked systems (MSNSs). The unknown input with no prior knowledge is introduced in the system...
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ISBN:
(纸本)9781665456579
In this paper, we investigate the simultaneous state and unknown input (SUI) filtering issue for a class of multi-sensor networked systems (MSNSs). The unknown input with no prior knowledge is introduced in the system state and output. The focus of this paper is to design the local filters with regard to the SUI, which can yield that the local upper bounds of the filtering error covariance for the SUI are derived at each instant. Moreover, the local filter gains of the SUI are designed such that the obtained upper bounds can be minimized. Finally, the proposed joint SUI algorithm is verified by using the simulation example.
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