Extremely large-scale hybrid reconfigurable intelligence surface(XL-HRIS),an improved version of the RIS,can receive the incident signal and enhance communication performance.However,as the RIS size increases,the phas...Extremely large-scale hybrid reconfigurable intelligence surface(XL-HRIS),an improved version of the RIS,can receive the incident signal and enhance communication performance.However,as the RIS size increases,the phase variations of the received signal across the whole array are nonnegligible in the near-field region,and the channel model mismatch,which will decrease the estimation accuracy,must be considered.In this paper,the lower bound(LB)of the estimated parameter is studied and the impacts of the distance and signal-tonoise ratio(SNR)on LB are then evaluated.Moreover,the impacts of the array scale on LB and spectral efficiency(SE)are also studied.Simulation results verify that even in extremely large-scale array systems with infinite SNR,channel model mismatch can still limit estimation accuracy.However,this impact decreases with increasing distance.展开更多
Model is usually necessary for the design of a control loop. Due to simplification and unknown dynamics, model plant mismatch is inevitable in the control loop. In process monitoring, detection of mismatch and evaluat...Model is usually necessary for the design of a control loop. Due to simplification and unknown dynamics, model plant mismatch is inevitable in the control loop. In process monitoring, detection of mismatch and evaluation of its influences are demanded. In this paper several mismatch measures are presented based on different model descriptions. They are categorized into different groups from different perspectives and their potential in detection and diagnosis is evaluated. Two case studies on mixing process and distillation process demonstrate the efficacy of the framework of mismatch monitoring.展开更多
基金supported in part by the National Natural Science Founda⁃tion of China(NSFC)under Grant Nos.62301148,62341107,and 62261160576by the Natural Science Foundation of Jiangsu Prov⁃ince under Grant No.BK20230824in part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Indus⁃try)under Grant Nos.BE2023022 and BE2023022-1.
文摘Extremely large-scale hybrid reconfigurable intelligence surface(XL-HRIS),an improved version of the RIS,can receive the incident signal and enhance communication performance.However,as the RIS size increases,the phase variations of the received signal across the whole array are nonnegligible in the near-field region,and the channel model mismatch,which will decrease the estimation accuracy,must be considered.In this paper,the lower bound(LB)of the estimated parameter is studied and the impacts of the distance and signal-tonoise ratio(SNR)on LB are then evaluated.Moreover,the impacts of the array scale on LB and spectral efficiency(SE)are also studied.Simulation results verify that even in extremely large-scale array systems with infinite SNR,channel model mismatch can still limit estimation accuracy.However,this impact decreases with increasing distance.
基金Supported in part by the National Basic Research Program of China (2012CB720500)the Erasmus Mundus Europe Asia(EMEA) Scholarship Program
文摘Model is usually necessary for the design of a control loop. Due to simplification and unknown dynamics, model plant mismatch is inevitable in the control loop. In process monitoring, detection of mismatch and evaluation of its influences are demanded. In this paper several mismatch measures are presented based on different model descriptions. They are categorized into different groups from different perspectives and their potential in detection and diagnosis is evaluated. Two case studies on mixing process and distillation process demonstrate the efficacy of the framework of mismatch monitoring.
文摘针对新能源汽车充电(G2V)三相整流器为研究对象,研究基于模型预测控制(model predictive control,MPC)的充电控制策略。传统的MPC算法需要准确的系统模型参数,而当控制器中使用的模型参数与主电路实际参数不匹配时,控制性能可能发生恶化,影响整流器充电控制性能。针对此问题,该文将系统参数不匹配作为扩张状态观测器(extended state observer,ESO)扩张出来的扰动项而进行估计,并将扰动进行补偿,从而设计一种基于ESO的MPC充电控制策略。该方法仅使用了系统的输入和输出数据,而不需要精确的系统模型,因此即使模型参数不匹配时,ESO也能够将不匹配项作为扰动而对预测电流进行准确估计,从而提高MPC对参数变化及不匹配的鲁棒性。仿真与实验结果验证了该方法的可行性和有效性。
