我所金尚泰老师课题组完成的论文《Compensation-Based Cooperative MFAILC for Multiple Subway Trains Under Asynchronous Data Dropouts》于2022-12在《IEEE Transactions on Intelligent Transportation Systems》正式发表。论文摘要如下：
Abstract: This paper researches the cooperative control problem for multiple subway trains under the asynchronous data dropouts in both measurement channel and downlink channel. The compensation-based cooperative model free adaptive iterative learning control (cCMFAILC) for the multiple city subway trains (MCSTs) is proposed to avoid deterioration of the control performance due to data dropouts. First, the nonlinear subway train system is transformed into an equivalent dynamic linearization data model to describe the input-output dynamics of the subway train system. Next, the lost data is replaced by the corresponding data of the same time instant in the latest available iteration. And the cCMFAILC is designed to guarantee that the speed tracking errors of MCSTs are bounded along the iteration axis and the headway of neighboring subway trains is stabilized in a safe range. Finally, theoretical analysis and MCSTs simulations verify the validity of the proposed cCMFAILC scheme.
引用信息：Qian Wang, Shangtai Jin, Zhongsheng Hou. Compensation-Based Cooperative MFAILC for Multiple Subway Trains Under Asynchronous Data Dropouts. IEEE Transactions on Intelligent Transportation Systems, 2022-12. DOI: 10.1109/tits.2022.3202911