Stochastic model predictive braking control for heavy-duty commercial vehicles during uncertain brake pressure and road profile conditions
Ryota Nakahara1
Kazuma Sekiguchi1
Kenichiro Nonaka1
Masahiro Takasugi2
Hiroki Hasebe2
Kenichi Matsubara2
1.Graduate School of Integrative Science and Engineering,Tokyo City University,Tamazutsumi,Setagaya-ku,Tokyo 1588557,Japan2.Nabtesco Automotive Co.,Ltd.,Hirakawacho,Chiyoda-ku,Tokyo 1020093,Japan
摘要：When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control.To minimize these uncertainties,we propose a combined longitudinal and lateral controller method based on stochastic model predictive control(SMPC)that is achieved via Chebyshev-Cantelli inequality.In our method,SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements,including chance constraints.To accomplish this,SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control strategy.The main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty effects.More specifically,the use of Chebyshev-Cantelli inequality suppresses road profile influences,which have characteristics that are different from the Gaussian distribution,thereby improving both braking robustness and control performance against statistical disturbances.Additionally,since the Kalman filtering(KF)algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints,the SMPC is reformulated as a KF embedded deterministic MPC.Herein,the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.
机标关键词：conditionspressureprofilecontrolmodelroadbrakebraking
论文发表日期：2022-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：15( 248-262 )
英文信息