Non-linear model-based predictive control of gasoline engine air-fuel ratio

1998 ◽  
Vol 20 (2) ◽  
pp. 103-112 ◽  
Author(s):  
B. Lennox ◽  
G.A. Montague ◽  
A.M. Frith ◽  
A.J. Beaumont
1999 ◽  
Vol 72 (10) ◽  
pp. 919-928 ◽  
Author(s):  
B. Kouvaritakis ◽  
M. Cannon ◽  
J. A. Rossiter

2021 ◽  
Vol 11 (10) ◽  
pp. 4687
Author(s):  
Philipp Maximilian Sieberg ◽  
Dieter Schramm

Considering automated driving, vehicle dynamics control systems are also a crucial aspect. Vehicle dynamics control systems serve as an important influence factor on safety and ride comfort. By reducing the driver’s responsibility through partially or fully automated driving functions, the occupants’ perception of safety and ride comfort changes. Both aspects are focused even more and have to be enhanced. In general, research on vehicle dynamics control systems is a field that has already been well researched. With regard to the mentioned aspects, however, a central control structure features sufficient potential by exploiting synergies. Furthermore, a predictive mode of operation can contribute to achieve these objectives, since the vehicle can act in a predictive manner instead of merely reacting. Consequently, this contribution presents a central predictive control system by means of a non-linear model-based predictive control algorithm. In this context, roll, self-steering and pitch behavior are considered as control objectives. The active roll stabilization demonstrates an excellent control quality with a root mean squared error of 7.6953×10−3 rad averaged over both validation maneuvers. Compared to a vehicle utilizing a conventional control approach combined with a skyhook damping, pitching movements are reduced by 19.75%. Furthermore, an understeering behavior is maintained, which corresponds to the self-steering behavior of the passive vehicle. In general, the central predictive control, thus, increases both ride comfort and safety in a holistic way.


2016 ◽  
Vol 39 (2) ◽  
pp. 208-223 ◽  
Author(s):  
Yiran Shi ◽  
Ding-Li Yu ◽  
Yantao Tian ◽  
Yaowu Shi

Modelling of non-linear dynamics of an air manifold and fuel injection in an internal combustion (IC) engine is investigated in this paper using the Volterra series model. Volterra model-based non-linear model predictive control (NMPC) is then developed to regulate the air–fuel ratio (AFR) at the stoichiometric value. Due to the significant difference between the time constants of the air manifold dynamics and fuel injection dynamics, the traditional Volterra model is unable to achieve a proper compromise between model accuracy and complexity. A novel method is therefore developed in this paper by using different sampling periods, to reduce the input terms significantly while maintaining the accuracy of the model. The developed NMPC system is applied to a widely used IC engine benchmark, the mean value engine model. The performance of the controlled engine under real-time simulation in the environment of dSPACE was evaluated. The simulation results show a significant improvement of the controlled performance compared with a feed-forward plus PI feedback control.


2001 ◽  
Vol 74 (4) ◽  
pp. 361-372 ◽  
Author(s):  
M. Cannon ◽  
B. Kouvaritakis ◽  
Y. I. Lee ◽  
A. C. Brooms

1992 ◽  
Vol 2 (3) ◽  
pp. 145-153 ◽  
Author(s):  
Suhas K. Mahuli ◽  
R. Russell Rhinehart ◽  
James B. Riggs

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