Biblio

This paper is concerned with the problem of adaptive finite-time prescribed performance control for a category of uncertain nonlinear systems subject to fuzzy dead-zone input. Via combining the technologies of command filter and backstepping control, the ``singularity'' and the ``explosion of complexity'' issues within controller design procedure are avoided. Moreover, by designing a state observer and utilizing the center-of-gravity theorem, the unmeasured states of system are estimated and the fuzzy issue result from fuzzy dead-zone input is disposed, respectively. Meanwhile, a finite-time fuzzy controller is constructed via combining with finite-time stability criterion, which guarantees all the signals in closed-loop system are convergent and the trajectory of tracking error also strictly evolves within a predefined range in finite time. At last, some simulation results confirm the viability of presented theoretical results.

Aiming at the composite uncertainty characteristics and high-dimensional data stream characteristics of the evaluation index with both ambiguity and randomness, this paper proposes a emergency severity assessment method for cluster supply chain based on cloud fuzzy clustering algorithm. The summary cloud model generation algorithm is created. And the multi-data fusion method is applied to the cloud model processing of the evaluation indexes for high-dimensional data stream with ambiguity and randomness. The synopsis data of the emergency severity assessment indexes are extracted. Based on time attenuation model and sliding window model, the data stream fuzzy clustering algorithm for emergency severity assessment is established. The evaluation results are rationally optimized according to the generalized Euclidean distances of the cluster centers and cluster microcluster weights, and the severity grade of cluster supply chain emergency is dynamically evaluated. The experimental results show that the proposed algorithm improves the clustering accuracy and reduces the operation time, as well as can provide more accurate theoretical support for the early warning decision of cluster supply chain emergency.