摘要
In order to solve problems of slow response speed and poor adaptability to working conditions,a model-based closed-loop control was proposed for the new type of electric variable valve system with continuously variable valve timing(VVT). Firstly,a parameter identification of Kalman filter based on innovation was used to identify the parameters of the motor system,and a control model of the variable gas distribution system was established using the identified parameters. Secondly,the output covariance constraint(OCC) control algorithm,a model-based control algorithm for the electric variable valve system was designed. Finally,after verified,the OCC algorithm was compared with the proportion integration differentiation(PID) algorithm and the liner quadratic tracking(LQT) algorithm in the simulation,and the control effect of the OCC controller and the PID controller was compared in the bench test. The simulation analysis result of the target phase adjustment at different speeds shows that the steady-state error of the PID adjustment at 1 000,3 000,and 4 000 r/min is greater than that of the OCC algorithm,and the adjustment speed of the LQT algorithm at 3 000 r/min and 4 000 r/min is smaller than that of the OCC algorithm,OCC algorithm has a better ability to maintain phase and less deviation at variable speeds. Compared with PID algorithm and LQT algorithm,OCC algorithm has better speed adaptability. The bench test result showed that,when the phase angle is adjusted upward,the response time of the OCC algorithm is reduced by 42.8% compared with the PID algorithm. When the phase angle is adjusted downward,the response time of the OCC algorithm is reduced by 75.0% compared with that of the PID algorithm.
投稿的翻译标题 | Output Covariance Control Strategy of an Electric Variable Valve System |
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源语言 | 繁体中文 |
页(从-至) | 325-333 |
页数 | 9 |
期刊 | Neiranji Xuebao/Transactions of CSICE (Chinese Society for Internal Combustion Engines) |
卷 | 42 |
期 | 4 |
DOI | |
出版状态 | 已出版 - 2024 |
关键词
- electric variable valve system
- output covariance control
- simulation analysis