摘要
火花点火-可控自燃(SI-CAI)混合燃烧对边界条件非常敏感,针对其燃烧相位和放热过程的瞬态控制难题,笔者提出一种基于燃烧模型的前馈、即时扰动观测反馈和累积观测校正相结合的主动抗扰自趋优控制算法.首先,通过引入点火角修正因子和模型参数自学习因子,发展了具备自学习能力的SI-CAI混合燃烧预测模型,并构建了CA 50、IMEP和过量空气系数φa的前馈控制算法.其次,为补偿模型的偏差和外部环境条件的随机干扰,提出了基于扩张状态观测器的扰动即时观测算法.最后,为不断校正模型参数自学习因子,改善前馈效果,提出基于递推最小二乘法的累积观测器.对提出的控制算法进行仿真和台架验证,结果表明:IMEP可在3~5个循环内完成阶跃,该过程中CA 50波动幅度不高于4°CA,φ_(a)偏差小于0.08,满足控制要求.
The spark-ignited-controlled auto-ignition(SI-CAI) hybrid combustion is highly sensitive to boundary conditions. To achieve smooth transient control of combustion timing and heat release process,an active disturbance rejection self-optimizing solution was proposed,which consists of a combustion model-based feedforward controller,a short-term disturbance observer and a long-term disturbance observer. First,a control-oriented SICAI hybrid combustion prediction model was developed by introducing an ignition timing correct factor and selflearning factors. A feedforward controller of CA 50,IMEP and excessive air coefficient φa was also developed.Then,a disturbance rejection controller based on extended state observer was adopted in order to compensate for the model deviation and random interference of environment conditions. Finally,an online model parameter learning controller based on recursive least square method was introduced in order to continuously correct the selflearning factors of model parameters and improve the feedforward effect. The proposed controller was validated in simulation and engine bench test. The results show that the actual IMEP converges to its desired value within 3D5cycles during step response test with a CA 50 deviation of less than 4° CA and φ_(a) deviation of less than 0.08.
作者
宋康
谢辉
Song Kang;Xie Hui(State Key Laboratory of Engines,Tianjin University,Tianjin 300350,China)
出处
《内燃机学报》
EI
CAS
CSCD
北大核心
2022年第6期543-551,共9页
Transactions of Csice
基金
国家重点研发计划资助项目(2017YFE01022800)
国家自然科学基金青年基金资助项目(51906174)。
关键词
汽油机
混合燃烧
火花点火-可控自燃(SI-CAI)
主动抗扰控制
自学习因子
gasoline engine
hybrid combustion
spark-ignited-controlled auto-ignition
active disturbance rejection control
self-learning factors
作者简介
宋康,博士,副教授,E-mail:songkangtju@tju.edu.cn;通信作者:谢辉,教授,E-mail:xiehui@tju.edu.cn.
