典型文献
Model Controlled Prediction:A Reciprocal Alternative of Model Predictive Control
文献摘要：
Dear editor,
This letter presents a reciprocal alternative to model predictive control(MPC),called model controlled prediction.More specifically,in order to integrate dynamic control signals into the transportation prediction models,a new fundamental theory of machine learning based prediction models is proposed.The model can not only learn potential patterns from historical data,but also make optimal predictions based on dynamic external control signals.The model can be used in two typical scenarios:1)For low real-time control signals(e.g.,subway timetable),we use a transfer learning method,so that the prediction models obtained from training data under the old control strategy can be predicted accurately under the new control strategy.2)For dynamic control signals with high real-time(e.g.,online ride-hailing dispatching instructions),we establish a simulation environment,design a control algorithm based on reinforcement learning(RL),and then let the model learn the mapping relationship among dynamic control signals,data,and output in the simulation environment.The experimental results show that the reasonable modeling of control signals can significantly improve the performance of the traffic prediction model.
文献关键词：
中图分类号：
作者姓名：
Shen Li;Yang Liu;Xiaobo Qu
作者机构：
Department of Civil Engineering,Tsinghua University,Beijing 100084,China;Department of Architecture and Civil Engineering,Chalmers University of Technology,Gothenburg SE-41296,Sweden;School of Vehicle and Mobility,Tsinghua University,Beijing 100084,China
文献出处：
引用格式：
[1]Shen Li;Yang Liu;Xiaobo Qu-.Model Controlled Prediction:A Reciprocal Alternative of Model Predictive Control)[J].自动化学报（英文版）,2022(06):1107-1110
A类：
timetable
B类：
Model,Controlled,Prediction,Reciprocal,Alternative,Predictive,Dear,editor,
This,letter,presents,reciprocal,alternative,predictive,MPC,called,controlled,More,specifically,order,integrate,dynamic,signals,into,transportation,models,new,fundamental,theory,machine,learning,proposed,not,only,potential,patterns,from,historical,data,but,also,make,optimal,predictions,external,be,used,two,typical,scenarios,For,low,real,subway,we,transfer,method,that,obtained,training,under,old,strategy,predicted,accurately,high,online,ride,hailing,dispatching,instructions,establish,simulation,environment,design,algorithm,reinforcement,RL,then,mapping,relationship,among,output,experimental,results,show,reasonable,modeling,significantly,improve,performance,traffic
AB值：
0.588615
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