1,723,255 research outputs found
Distributed iterative learning control for networked dynamical systems
No full textNetworked dynamical systems have found increasingly more applications during the lastfew decades, thanks to the significant reduction in the cost of sensing, computing and actuating technologies. Among them, there exists a class of networked dynamical systemsworking in a repetitive manner and requiring high control performance. As an example,next generation advanced manufacturing contains a large number of subsystems workingtogether to perform a variety of manufacturing tasks repeatedly with high performancerequirements. For such systems, traditional control methods have significant difficultiesmeeting the high performance requirements: centralised design does not scale well, whiledistributed methods mainly focus on asymptotic behaviour. In addition, they all requirea highly accurate model which can be difficult/expensive to obtain in practice.Recently, iterative learning control (ILC), which ‘learns’ from the input and error information of the previous attempts of the same task without requiring an accurate modelto generate the input, has been proposed as an alternative solution. However, most ofthe existing ILC design for networked dynamical systems have poor scalability, limitedconvergence performance, and also lack of the ability to deal with system constraintsand more general task, e.g., point-to-point (P2P) task. To address these limitations, thisthesis proposes novel distributed/decentralised optimisation-based ILC design methods.This thesis considers three design problems for networked dynamical systems, i.e., consensus tracking, formation control and collaborative tracking. We propose optimisationbased ILC design methods using the idea of norm optimal ILC, and the proposed ILCmethods show appealing convergence properties and certain degree of robustness tomodel uncertainties. Using the alternating direction method of multipliers (ADMM),all the designs can be implemented in the distributed/decentralised manner such thatonly local information is needed, allowing the proposed algorithms to be applied to largescale networked dynamical systems and have great scalability for dynamically growingnetwork. These algorithms can also be extended to solve two unexplored problems inILC design for networked dynamical systems, namely, constraint handling and P2P task.Numerical examples are given to illustrate the performance of the proposed algorithm
A study on the genus Lagria from China with one new species and new distributional records (Coleoptera: Lagriinae)
No full textZhou, Yong, Chen, Bin (2023): A study on the genus Lagria from China with one new species and new distributional records (Coleoptera: Lagriinae). Zootaxa 5254 (3): 413-424, DOI: 10.11646/zootaxa.5254.3.7, URL: http://dx.doi.org/10.11646/zootaxa.5254.3.
Distributed iterative learning control for high performance consensus tracking problem with switching topologies
No full textHigh performance consensus tracking problem operating repetitively has attracted significant research interest in different fields. Recent research apply iterative learning control (ILC) for such problems, since ILC does not require a highly accurate model to achieve the high accuracy requirement (which is in contrast to most of the conventional control methodologies). However, existing ILC designs for high performance consensus tracking problem either focus on the tracking under fixed topology (while the switching topologies structure that is common used in reality has not been taken into account), or can only guarantee the convergence performance when the controller satisfies certain conditions. To address these limitations, this paper proposes a novel ILC algorithm for the high performance consensus tracking problem with switching topologies. The design of the novel performance index guarantees monotonic convergence of the tracking error norm to zero without any restriction on the controller. Furthermore, the proposed algorithm is suitable for homogeneous and heterogeneous networked systems, which is appealing in practice. A distributed implementation using the idea of the alternating direction method of multiplies for the proposed algorithm is provided, allowing the algorithm to be applied to large scale networked dynamical systems. Convergence properties of the algorithm are analysed rigorously and numerical examples are presented to show the algorithm's effectiveness.</p
Distributed norm optimal iterative learning control for high performance consensus tracking
No full textHigh performance consensus tracking problem, which requires all the subsystems operating repetitively to track a desired reference, has found a number of important applications in the last decade. To achieve the high performance requirement, recent designs use iterative learning control (ILC) to avoid the use of an accurate model that is usually required in conventional control methods. However, most of the existing distributed ILC algorithms have poor scalability (i.e., they will have difficulties when applied to large scale and/or changing networks). Their performance (e.g., monotonic tracking error norm convergence) is heavily dependent on the choice of control parameters and they cannot handle general point-to-point tasks either. To address these limitations, this article proposes a novel distributed ILC algorithm using the well-known norm optimal ILC framework. By designing a performance index that explicitly incorporates the convergence performance, the resulting ILC design guarantees the tracking error norm converges monotonically to zero, which is appealing in practice. Using the alternating direction method of multipliers, a distributed implementation of the algorithm is obtained, where each subsystem's input is updated locally, such that the algorithm can be applied to large scale and/or changing networks without any issues. Furthermore, the proposed algorithm can be extended to solve point-to-point consensus tracking problem, and applied to both homogeneous and heterogeneous networks, as well as non-minimum phase systems, which is of great practical relevance. Convergence and robustness of the algorithms are analysed rigorously. Numerical examples are given to verify the effectiveness of the proposed algorithms
Predictive norm optimal iterative learning control for high-performance formation control problem
No full textThis paper develops a predictive optimisationbased iterative learning control (ILC) strategy for the highperformance formation control problem in networked dynamical systems working repetitively. It avoids the need for exact model information in traditional methods and achieves high performance via a predictive framework incorporating a unique performance index that integrates both immediate and future performance. The proposed framework guarantees geometric convergence of the formation error norm to zero and is capable of handling both heterogeneous and non-minimum phase systems. A distributed implementation of the framework is developed using the Alternating Direction Method of Multipliers to guarantee the framework’s scalability for largescale networks. Rigorous convergence analysis and numerical examples are provided to confirm its effectiveness
Two newly recorded genera Malayepipona Giordani Soika and Megaodynerus Gusenleitner, with eight new species from China (Hymenoptera, Vespidae, Eumeninae)
No full textBai, Yue, Chen, Bin, Li, Ting-Jing (2021): Two newly recorded genera Malayepipona Giordani Soika and Megaodynerus Gusenleitner, with eight new species from China (Hymenoptera, Vespidae, Eumeninae). Zootaxa 5060 (3): 371-391, DOI: https://doi.org/10.11646/zootaxa.5060.3.
A revision of the genus Jucancistrocerus Blüthgen, 1938 from China, with review of three related genera (Hymenoptera: Vespidae: Eumeninae)
No full textLi, Ting-Jing, Bai, Yue, Chen, Bin (2022): A revision of the genus Jucancistrocerus Blüthgen, 1938 from China, with review of three related genera (Hymenoptera: Vespidae: Eumeninae). Zootaxa 5105 (3): 401-420, DOI: 10.11646/zootaxa.5105.3.
Dabieshan UHP metamorphic terrane: Sr-Nd-Pb isotopic constraint to pre-metamorphic subduction polarity
No full textA taxonomic revision of Allodynerus Blüthgen (Hymenoptera: Vespidae: Eumeninae) from China
No full textZhang, Xue, Chen, Bin, Li, Ting-Jing (2020): A taxonomic revision of Allodynerus Blüthgen (Hymenoptera: Vespidae: Eumeninae) from China. Zootaxa 4750 (4): 545-559, DOI: https://doi.org/10.11646/zootaxa.4750.4.
FIGURES 8–11 in Review of Chinese species of the genus Embrikstrandia Plavilstshikov, 1931 (Coleoptera: Cerambycidae: Cerambycinae) with description of a new species
No full textFIGURES 8–11. Embrikstrandia unifasciata (Ritsema), dorsal habitus, showing individuals having antennae with seven ochraceous apical segments.Published as part of Huang, Jianhua, Zhou, Shanyi & Chen, Bin, 2006, Review of Chinese species of the genus Embrikstrandia Plavilstshikov, 1931 (Coleoptera: Cerambycidae: Cerambycinae) with description of a new species, pp. 57-68 in Zootaxa 1340 on page 64, DOI: 10.5281/zenodo.17440
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