A Survey of Model Predictive Control Development in Automotive Industries

被引:0
作者
Swief, Asmaa [1 ]
El-Zawawi, Amr [1 ]
El-Habrouk, Mohamed [1 ]
机构
[1] Alexandria Univ, Fac Engn, Elect Engn Dept, Alexandria, Egypt
来源
2019 3RD INTERNATIONAL CONFERENCE ON APPLIED AUTOMATION AND INDUSTRIAL DIAGNOSTICS (ICAAID 2019) | 2019年
关键词
Model Predictive Control; Vehicle Control; Autonomous Vehicles; Adaptive Cruise Control; Collision Avoidance; Vehicle Dynamics; Real-Time Implementation; Fast Systems; Embedded Systems; Neural Network Control; Linear Programming; Hybrid Electric Vehicle(HEV); HYBRID ELECTRIC VEHICLE; ENGINE; MANAGEMENT; STABILIZATION; MPC; YAW;
D O I
10.1109/icaaid.2019.8934974
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Model Predictive Control (MPC) technology has been progressed steadily over 30 years ago. MPC applications are currently showing a stable architecture in many industries "e.g. Chemicals, Aerospace, Defense, and Food processing". Recently, the work area of MPC's technologies is changing fast, so it's difficult to preserve track of the rapid progress in industrial applications and academic research. MPC is currently showing huge potential for being used in automotive applications as vehicles subsystems will be progressively coordinated to enhance fuel economy and safety. Thus, novel chances for MPC will arise, including coordination of braking action and powertrain in torque vectoring, coordination of engine functionality and transmission to enhance fuel consumption and reaction, control of complex engines. Being used at a supervisory level, more interaction of MPC with the driver is expected. Thus, a major challengeable research experiment for MPC will be to include a driver prediction model. This is already an ongoing effort. In the other side, the active front steering (AFS) braking controllers have been developed with a more detailed prediction model of the driver steering behavior. Accordingly, the recent developed controllers are guaranteeing the same stability performance, are much more predictable to drive. Likewise, an energy controlling approach is recently suggested, where the driver behavior is modelled as a Markov Chain learned in real-time, and used in a stochastic MPC algorithm. The resulting strategy adapts to the way the car is driven, to the drive cycle, and to the environment, achieving economic fuel consumption close to the one obtained with future information.
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页数:7
相关论文
共 68 条
[1]  
Alessandretti Andrea, 2013 EUR CONTR C ECC, DOI [10.23919/ECC.2013.6669717, DOI 10.23919/ECC.2013.6669717.]
[2]  
Ali Z, 2012, COMM COM INF SC, V281, P81
[3]  
Amari R., 2008, IFAC P VOLUMES, V41, P7079, DOI 10.3182/20080706-5-KR-1001.01200
[4]  
[Anonymous], ALGORITHM HARDWARE I
[5]  
Aumeister T. H. B., 2017, DEEP LEARNING MODEL, P1
[6]  
Baca T, 2016, 2016 21ST INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN AUTOMATION AND ROBOTICS (MMAR), P992, DOI 10.1109/MMAR.2016.7575273
[7]  
Balakrishna V., ROBUST CONSTRAINED M
[8]   Model Predictive Control for Vehicle Stabilization at the Limits of Handling [J].
Beal, Craig Earl ;
Gerdes, J. Christian .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 21 (04) :1258-1269
[9]   The explicit linear quadratic regulator for constrained systems [J].
Bemporad, A ;
Morari, M ;
Dua, V ;
Pistikopoulos, EN .
AUTOMATICA, 2002, 38 (01) :3-20
[10]   Nonlinear Model Predictive Control for Power-split Hybrid Electric Vehicles [J].
Borhan, H. Ali ;
Zhang, Chen ;
Vahidi, Ardalan ;
Phillips, Anthony M. ;
Kuang, Ming L. ;
Di Cairano, S. .
49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, :4890-4895