The proton exchange membrane (PEM) fuel cells have attracted significant attention due to high efficiency and low pollution. The performance and lifetime of the fuel cell power system depend on the heat and water mana...
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The agents connected by networks are capable to reach a prescribed state if only a small fraction of them are controlled with feedback information. The present work tries to stabilize groups of non-linear agents in a ...
A blind least-mean-squares (BLMS) algorithm is proposed for the parameter identification of single-input multiple-output (SIMO) systems. Without requiring knowledge of a reference signal, it is proved that the present...
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Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN...
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Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN) to improve the measurement accuracy of electronic compass. This method uses Fourier neural network to model electronic compass error, and adopts Adaptive Differential Evolution to optimize the weights of neural network, and get more exact error model to compensate measured values. The compensation object is the common electronic compass composed by two-dimensional magnetic resistance sensor. Compared with the compensation effect of Least-square method, BP neural network and Fourier neural networks, It proves that the mode of this method can realize the high precision in the sample space mapping and high non-linear approximation ability, and this method has faster convergence rate, can avoid falling into local minima, reduces the training error, and improves error compensation accuracy. This method decreases the error range from -3.4° ~ 25.2° before compensation to -0.20° ~ 0.72°, and the average of the absolute error is 0.30°. Repeatability tests also proved the compensation plan have a good consistency.
Abstract This paper addresses the problem of distributed connectivity constrained motion coordination of multiple autonomous mobile agents. Different from traditional flat network structure which lacks flexibility and...
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Abstract This paper addresses the problem of distributed connectivity constrained motion coordination of multiple autonomous mobile agents. Different from traditional flat network structure which lacks flexibility and scalability when performing complex spatially distributed tasks, a novel distributed framework for construction of backbone-based hierarchical communication networks is proposed. Firstly, the proposed method periodically extracts a subset of agents which can form the communication backbone from the original network using only local information, thus partitions the system into backbone agents and non-backbone agents. Furthermore, the global network connectivity of the system is maintained at two levels: connectivity-preserving potential functions are used to maintain existing links in the backbone; connectivity between backbone and non-backbone agents is achieved via a leader-follower formation control scheme with backbone agents as the leaders. Finally, nontrivial numerical simulations are worked out to verify the theoretical results.
An efficient evaluation index system and evaluation model is crucial for effectiveness evaluation of the air defense networked fire controlsystem. This paper, based on pre-research for functions and characteristics o...
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Recognizing various traffic signs, especially the popular circular traffic signs, is an essential task for implementing advanced driver assistance system. To recognize circular traffic signs with high accuracy and rob...
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An average consensus protocol is proposed for discrete-time double-integrator multi-agent systems with communication noises under fixed topologies. The proposed consensus protocol is composed of two parts: the agent&...
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An average consensus protocol is proposed for discrete-time double-integrator multi-agent systems with communication noises under fixed topologies. The proposed consensus protocol is composed of two parts: the agent's own state feedback and the relative states between the agent and its neighbor agents. Due to the existence of communication noises, the relative states cannot be obtained accurately. To attenuate the noise effect, a time-varying consensus gain a(k) is applied to the relative states in the proposed protocol. Hence, the closed-loop dynamics of multi-agent systems is a linear stochastic difference equation with variable coefficients. Fortunately, the state transition matrix of this stochastic system can be solved, and the dynamical behavior of linear multi-agent systems can therefore be determined. It is proved that the proposed protocol is able to solve the mean square average consensus problem if and only if the topology graph is connected;and the time-varying gain a(k) satisfies the stochastic-approximation type conditions (Omission)。
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