This paper focuses on the problem of the gait planning of a hexapod robot in order to improve its practicability. Gait planning based on the torque constraints and the stability margin constrains is proposed. The gait...
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This paper focuses on the problem of the gait planning of a hexapod robot in order to improve its practicability. Gait planning based on the torque constraints and the stability margin constrains is proposed. The gait planning on the ground plane is introduced with combining with the mechanism specification of the hexapod robot, and the torque constraints and the stability margin constrains on the ground plane is proposed with the kinematic analysis and force analysis. Then the key constraints for gait planning on the slope are proposed to deal with a more realistic problem. The algorithm analysis can prove the variation of the keycontrol variable under the two constrains. Simulation results are presented to support the proposed algorithm.
This paper considers using reset control to improve transient performance and overcome some fundamental limitations of linear systems. First, an auxiliary system is presented, then, based on which, a new reset control...
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This paper considers using reset control to improve transient performance and overcome some fundamental limitations of linear systems. First, an auxiliary system is presented, then, based on which, a new reset control model is proposed, such that the non-overshoot performance specification can be met for any minimum phase relative degree one plants, the results imply some limitations of linear systems are overcome and clearly illustrate the advantages of reset control. A numerical example is given to show the effectiveness.
Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensin...
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Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are ***, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.
Multichannel MAC protocol (MMAC) was proposed by So et al. It divides time into beacon periods and consists of an announcement traffic information message (ATIM) phase and a data phase (So et al. Proceedings of 5th AC...
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作者：
Guiyuan FuWeidong ZhangDepartment of Automation
Shanghai Jiaotong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 P. R. China
The continuous opinion dynamics with group-based heterogeneous bounded confidences is considered in this paper. Firstly, a slightly modified Hegselmann-Krause model is proposed, and the agents are divided into open-mi...
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The continuous opinion dynamics with group-based heterogeneous bounded confidences is considered in this paper. Firstly, a slightly modified Hegselmann-Krause model is proposed, and the agents are divided into open-minded-, moderate-minded-, and close-minded-subgroups according to the corresponding confidence intervals. Then numerical simulations are carried out to analyze the influence of the close-minded and open-minded agents, as well as the population size, on the opinion dynamics. It is observed that (1) for the fixed population size, the larger proportion of close-minded agents, the more opinion clusters; (2) open-minded agents cannot contribute to forging different opinions, instead, the existence of them maybe diversify final opinions; also interestingly the relative size of the largest cluster varies along concave-parabola-like curve as the proportion of open-minded agents increases; (3) for the same proportion of the three subgroups, as population size increases, the number of final opinion clusters will increase at the beginning and then reach a stable level, which is quite different from the previous studies.
Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired...
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Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired from the simulation and evaluation system formed as intervalvalued informationsystem （IIS） is classified by the interval similarity relation. Then, as an improvement of the classical rough set, a new kind of generalized information entropy called ＂H＇-information entropy＂ is suggested for the measurement of uncertainty and the classification ability of IIS. There is an innovative information filling technique using the properties of H＇-information entropy to replace missing data by some smaller estimation intervals. Finally, an improved method of failure analysis synthesized by the above achievements is presented to classify the thruster experimental data, complete the information, and extract the failure rules. The feasibility and advantage of this method is testified by an actual application of failure analysis, whose performance is evaluated by the quantification of E-condition entropy.
This paper addresses the problem of infinite time performance of model predictive controllers applied to constrained nonlinear systems. The total performance is compared with a finite horizon optimal cost to reveal pe...
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Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. ...
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Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed *** on a 10-million image database further reveals the scalability of our approach.
This paper deals with the consensus problem for heterogeneous multi-agent systems. Different from most existing consensus protocols, we consider the consensus seeking of two types of agents, namely, active agents and ...
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This paper deals with the consensus problem for heterogeneous multi-agent systems. Different from most existing consensus protocols, we consider the consensus seeking of two types of agents, namely, active agents and passive agents. The objective is to directly control the active agents such that the states of all the agents would achieve consensus. In order to obtain a computational approach, we subtly introduce an appropriate Markov chain to cast the heterogeneous systems into a unified framework. Such a framework is helpful for tackling the constraints from passive agents. Furthermore, a sufficient and necessary condition is established to guarantee the consensus in heterogeneous multi-agent systems. Finally, simulation results are provided to verify the theoretical analysis and the effectiveness of the proposed protocol.
In the brand new era of “big data”, finding creative strategies for information fusion in a complicated system is important. Previous researchers have introduced a model called “Local Strongly Coupled system”, in ...
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In the brand new era of “big data”, finding creative strategies for information fusion in a complicated system is important. Previous researchers have introduced a model called “Local Strongly Coupled system”, in which every agent stochastically communicates only with its neighbours (i.e. “coupled” nodes). However, the meaning of a system's intrinsic a priori constraints is always neglected. And we assume that in practical instances, the whole system is supposed to take on a consistent result, or “consensus” decision. This paper, taking constraints into account, presents a new strategy to help minimize the filtering error. Furthermore, based on consensus policy, the approach is applied to local strongly coupled systems, especially systems with packet loss. Effectiveness and practicability of all the proposed algorithms are shown through simulations.
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