Previously, we proposed a topological-based swarming model in which agents probabilistically decide which other agents to interact with based on the proximity of positions of agents. Agents then average their directio...
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Previously, we proposed a topological-based swarming model in which agents probabilistically decide which other agents to interact with based on the proximity of positions of agents. Agents then average their direction with the directions of the agents they have interacted with. In this paper, we improve the model mentioned-above by taking the influence of similarity of directions into consideration, and the former model can be considered as a special case of this model here. We propose a probabilistic method which depends on both proximity of agents' positions and similarity of agents' moving directions. And agents are more likely to form links with those agents who carry a similar direction to theirs and those agents who are proximal to them. We show that there exits a non-zero positive lower bound of the selection probability, and the system can be connected in probability, which ensures the system's achievement of swarming. And by simulations, it is shown that the rate of getting alignment exhibit a strong correlation with the parameters of the system which are weighting factor, neighborhood size, proximity factor and similarity factor.
作者：
Xiaofan WangXiaoling WangDepartment of Automation
Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China
Existed works on consensus in networks have been focused on reaching an agreement among states of nodes in a network. In this work, we propose a discrete-time edge consensus protocol for complex networks. By mapping t...
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ISBN:
(纸本)9781479934331
Existed works on consensus in networks have been focused on reaching an agreement among states of nodes in a network. In this work, we propose a discrete-time edge consensus protocol for complex networks. By mapping the edge topology into a corresponding line graph, we prove that consensus can be achieved among the states of all edges in a connected network. Theoretical analysis and simulation results are provided to show the effectiveness of the model and the influence of network topology.
Walking on irregular terrain is usually a common task for a quadruped robot. It is however difficult to control the robot in this situation as undesirable impulse force by collision between the foot of robot and obsta...
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This paper considers the stabilization problem for Markovian jump systems with time delays. Both the probability rate matrix and the state feedback control law are to be designed. A sufficient condition is established...
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This paper considers the stabilization problem for Markovian jump systems with time delays. Both the probability rate matrix and the state feedback control law are to be designed. A sufficient condition is established for such designs such that the resulting closed-loop Markovian jump system is stochastically stable. This condition is given in terms of a system of linear matrix inequalities with rank constraints, and can be solved using some existing algorithms. When the system has polytopic uncertainties, the robust stabilization problem is studied as well. Finally, a numerical example is given to show the validity of the proposed method.
This paper designs multi-step probabilistic sets for linear, discrete-time, stochastic systems with unbounded multiplicative noise and probabilistic constraints. Multi-step probabilistic sets strengthen IWPp by bringi...
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This paper designs multi-step probabilistic sets for linear, discrete-time, stochastic systems with unbounded multiplicative noise and probabilistic constraints. Multi-step probabilistic sets strengthen IWPp by bringing more degrees of freedom to optimize the applicable region of finite-step probabilistic constraints, and extending the prediction horizon of IWPp to infinity for infinite-horizon probabilistic constraints. Conditions for multi-step probabilistic sets are then incorporated into a stochastic model predictive control algorithm to satisfy probabilistic constraints. Closed-loop mean-square stability is guaranteed by the algorithm. A numerical example shows the performance of the proposed algorithm.
This paper focuses on the problem of secure distributed consensus to defend covert misbehavior in wireless sensor networks (WSNs). Distributed consensus is a promising method to improve the efficiency and precision of...
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This paper focuses on the problem of secure distributed consensus to defend covert misbehavior in wireless sensor networks (WSNs). Distributed consensus is a promising method to improve the efficiency and precision of consensus results in WSNs, but it introduces new security issues that malicious nodes may manipulate false sensing data to degrade the sensing result of the whole network. A data falsification attack, i.e., the attacker injects random values into its neighboring nodes at each time-step of consensus process, is considered. This kind of attack cannot be defended against by most of existing detection mechanisms. We present a distributed detection mechanism with adaptive local threshold to isolate the abnormal nodes. A Weighted Averaging-based Consensus Scheme (WACS) is proposed to decrease the negative impact of the attack and make the network converge to a consensus value. It is proved that convergence property can be guaranteed by the relationship between weighted average of the noise and stochastic approximation. Simulation results are presented to show the effectiveness of the proposed secure scheme.
This paper is concerned with stochastic model predictive control for Markovian jump linear systems with additive disturbance, where the systems are subject to soft constraints on the system state and the disturbance s...
This paper is concerned with stochastic model predictive control for Markovian jump linear systems with additive disturbance, where the systems are subject to soft constraints on the system state and the disturbance sequence is finitely supported with joint cumulative distribution function given. By resorting to the maximal disturbance invariant set of the system, a model predictive control law is given based on a dynamic controller which is with guaranteed recursive feasibility and ensures the probabilistic constraints on the states. By optimizing the volume of the disturbance invariant set, the dynamic controller is given. The closed loop system under this control law is proven to be stable in the mean square sense. Finally, a numerical example is given to illustrate the developed results.
3D human pose reconstruction is a key concern in computer vision area in recent *** to the deficiency of depth information,reconstructing human pose from a single image or image sequences is still a difficult and chal...
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3D human pose reconstruction is a key concern in computer vision area in recent *** to the deficiency of depth information,reconstructing human pose from a single image or image sequences is still a difficult and challenging *** this paper,we present an annealed particle filter algorithm based on reprojection error to recover the 3D configuration of human upper body,with the annotated joints’position in the *** addition,we make simplifications to the weak perspective projection,and the 7 camera parameters to be estimated are reduced to only *** show that our method is simple but exactly suitable for recovering articulated human upper body pose.
The property of single prediction predictive control in the form of dynamic matrix control is studied within internal model control framework. The sensitivity function and integral squared error are used as performanc...
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The property of single prediction predictive control in the form of dynamic matrix control is studied within internal model control framework. The sensitivity function and integral squared error are used as performance evaluation criteria in the frequency and time domain respectively, to quantitatively analyze single prediction strategy, especially on controller with the prediction and control horizon P = M = 1. We present the correlation between system performance and model mismatch in this case. The performance limitation for tracking unit step signal is obtained through derivation and simulation.
In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks ...
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In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.
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