As we know, the dynamic visual-tracking control, which is based on the depth-independent interaction matrix, has been proposed to cope with the general 3-D motion of robot manipulators. To deal with the unknown camera...
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As we know, the dynamic visual-tracking control, which is based on the depth-independent interaction matrix, has been proposed to cope with the general 3-D motion of robot manipulators. To deal with the unknown camera parameters, an adaptive law has been designed. It is noted, however, that the designed adaptive law uses a vector signal defined by the true depths of feature points, which are unavailable when the camera parameters are unknown. Nevertheless, such an adaptive law can still be implemented by replacing the exact depths with the estimated depths, since the estimated depths can be readily calculated by using the estimated values of the camera parameters. In this case, however, we cannot definitely say whether or not the robot system can be guaranteed to achieve asymptotical stability or even stable behavior. To overcome this problem, in this paper, we redefine the mentioned vector signal using the estimated depths and show that the design based on the new vector signal can make the robot system asymptotically stable. Additionally, the existing tracking control design based on the concept of depth-independent interactionmatrix is initial-state dependent. Thus, in this paper, we also modify the existing design to obtain an initial-state-independent result. To show the performance of the proposed designs, simulation results based on a two-link planar manipulator are presented. In addition, preliminary experimental results using an industrial manipulator are also given.
Motivation: The rapid growth of DNA sequence data creates a need to find graphic tools to study DNA sequence in a perceivable form. A large number of scholars work hard in this field. However, it is difficult to handl...
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Motivation: The rapid growth of DNA sequence data creates a need to find graphic tools to study DNA sequence in a perceivable form. A large number of scholars work hard in this field. However, it is difficult to handle the following problems in one graph: (i) degeneracy, (ii) loss of information, (iii) difficulty of observing in multi-dimensional graph, (iv) difficulty of visualization when representing long DNA sequences and (v) need to reflect useful information. Results: DV-Curve (Dual-Vector Curve) using two vectors to represent one alphabet of DNA sequences not only avoids degeneracy and loss of information, but also has good visualization no matter whether sequences are long, and can reflect the length of DNA sequence. The applications of the DV-Curve on mutation analysis and two types of similarity analysis are presented in detail. DV-Curve is a significative tool by which biologists could find useful biological knowledge.
In this paper, we investigate stochastic suppression and stabilization for a class of non-autonomous differential systems. Given a deterministic non-autonomous differential system, we introduce two independent Brownia...
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In this paper, we investigate stochastic suppression and stabilization for a class of non-autonomous differential systems. Given a deterministic non-autonomous differential system, we introduce two independent Brownian motions and perturb this system into a new stochastic differential system. By using Lyapunov analysis method and some stochastic techniques, we show that a polynomial Brownian noise may guarantee the existence of global solution of the perturbed system while another linear Brownian noise may stabilize this system with general decay rate. An application of stochastic stabilization of differential system in the modeling of population growth is indicated. (C) 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
This paper addresses model predictive control schemes for consensus in multi-agent systems (MASS) with discrete-time single-integrator dynamics under switching directed interaction graphs. The control horizon is exten...
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This paper addresses model predictive control schemes for consensus in multi-agent systems (MASS) with discrete-time single-integrator dynamics under switching directed interaction graphs. The control horizon is extended to be greater than one which endows the closed-loop system with extra degree of freedom. We derive sufficient conditions on the sampling period and the interaction graph to achieve consensus by using the property of infinite products of stochastic matrices. Consensus can be achieved asymptotically if the sampling period is selected such that the interaction graph among agents has a directed spanning tree jointly. Significantly, if the interaction graph always has a spanning tree, one can select an arbitrary large sampling period to guarantee consensus. Finally, several simulations are conducted to illustrate the effectiveness of the theoretical results. (C) 2015 ISA. Published by Elsevier Ltd. All rights reserved.
This paper addresses the bipartite flock control problem where a multi-agent system splits into two clusters upon internal or external excitations. Using structurally balanced signed graph theory, LaSalle's invari...
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This paper addresses the bipartite flock control problem where a multi-agent system splits into two clusters upon internal or external excitations. Using structurally balanced signed graph theory, LaSalle's invariance principle and Barbalat's Lemma, we prove that the proposed algorithm guarantees a bipartite flocking behavior. In each of the two disjoint clusters, all individuals move with the same direction. Meanwhile, every pair of agents in different clusters moves with opposite directions. Moreover, all agents in the two separated clusters approach a common velocity magnitude, and collision avoidance among all agents is ensured as well. Finally, the proposed bipartite flock control method is examined by numerical simulations. The bipartite flocking motion addressed by this paper has its references in both natural collective motions and human group behaviors such as predator-prey and panic escaping scenarios. (C) 2013 Elsevier B. V. All rights reserved.
This study addresses the cooperative control problem of multi-missile systems. It proposed a two-stage control strategy, aiming at simultaneous attack from a group of missiles at a static target. The first stage adopt...
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This study addresses the cooperative control problem of multi-missile systems. It proposed a two-stage control strategy, aiming at simultaneous attack from a group of missiles at a static target. The first stage adopts a special distributed consensus protocol in order for all missiles to asymptotically achieve a consensus of states. During the second stage, the local sightline control law allows the missiles to independently reach the target. The dynamic equation of the missile agent is normalised to a quasi-double-integrator model which is convenient for designing the consensus protocol. The proposed strategy is suitable for missiles of different speeds that have been self-organised without air operations centres. Two convincing simulation results are given to illustrate the efficiency of the proposed method.
In this paper, the consensus problem for a class of interconnected systems with different cyber-physical topologies is investigated. A two-layer control framework is proposed where two different connections exist amon...
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In this paper, the consensus problem for a class of interconnected systems with different cyber-physical topologies is investigated. A two-layer control framework is proposed where two different connections exist among the systems in physical and cyber layers, respectively. The former directly reflects the physical coupling among systems, while the latter uses the states' information transferred by communication channels to generate control. The physical and cyber topologies are undirected and not required to be connected. An event-triggered protocol based on cyber local sampled information is designed with no need to solve any matrix equation or inequality. Theoretical sufficient conditions for consensus are derived in terms of algebraic inequality and Zeno behavior is excluded. A novel expandable construction scheme is proposed to construct switching topologies for cyber layer. Numerical simulation and comparison show the effectiveness of the proposed control method and the advantage on saving communication network constructions.
This paper deals with the global exponential stability for delayed recurrent neural networks (DRNNs). By constructing an augmented Lyapunov-Krasovskii functional and adopting the reciprocally convex combination approa...
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This paper deals with the global exponential stability for delayed recurrent neural networks (DRNNs). By constructing an augmented Lyapunov-Krasovskii functional and adopting the reciprocally convex combination approach and Wirtinger-based integral inequality, delay-dependent global exponential stability criteria are derived in terms of linear matrix inequalities. Meanwhile, a general and effective method on global exponential stability analysis for DRNNs is given through a lemma, where the exponential convergence rate can be estimated. With this lemma, some global asymptotic stability criteria of DRNNs acquired in previous studies can be generalized to global exponential stability ones. Finally, a frequently utilized numerical example is carried out to illustrate the effectiveness and merits of the proposed theoretical results.
This paper is concerned with the problem of simultaneously estimating the state and the input of a linear discrete-time system. In order to improve the accuracy of estimation, a reasonable assumption about the unknown...
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This paper is concerned with the problem of simultaneously estimating the state and the input of a linear discrete-time system. In order to improve the accuracy of estimation, a reasonable assumption about the unknown input is proposed. Based on this assumption, a modified input estimation is obtained, and then the optimal state estimation is derived strictly. Moreover, in order to effectively deal with the possible saltations of the unknown input, an adaptive estimation method is also developed. Simulation results illustrate the effectiveness and applicability of the proposed approach.
A new restoration algorithm based on double loops and alternant iterations is proposed to restore the object image effectively from a few frames of turbulence-degraded images, Based on the double loops, the iterative ...
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A new restoration algorithm based on double loops and alternant iterations is proposed to restore the object image effectively from a few frames of turbulence-degraded images, Based on the double loops, the iterative relations for estimating the turbulent point spread function PSF and object image alternately are derived. The restoration experiments have been made on computers, showing that the proposed algorithm can obtain the optimal estimations of the object and the point spread function, with the feasibility and practicality of the proposed algorithm being convincing.
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