Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6954091 | Mechanical Systems and Signal Processing | 2018 | 15 Pages |
Abstract
In this paper, a neural-network-based nonlinear model predictive control (NMPC) scheme is investigated to realize coordinated control over the throttle and wastegate of a turbocharged gasoline engine of a passenger vehicle. First, due to the presence of MAPs and the complex structure of the turbocharged engine, establishing a mechanism model for controller design is very complicated. Benefiting from a large amount of experimental data, a predictive model is learned by a neural network to predict the future dynamics of the engine air-path system, and the accuracy of this model is verified. Second, to address the system constraints and coupling, a nonlinear model predictive controller is proposed to track the desired intake manifold pressure and boost pressure for meeting the engine torque demand. Third, quantum-behaved particle swarm optimization (QPSO) is applied for optimization of the NMPC objective function to obtain a more accurate solution. Finally, the performance of the control system is tested using the commercial simulation software AMESim.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Yunfeng Hu, Huan Chen, Ping Wang, Hong Chen, Luquan Ren,