基于极值搜索的柴油机空气系统模型预测控制

Model Predictive Control of Diesel Engine Air System Based on Extremum Search

针对配备可变截面涡轮增压器(VGT)、高压废气再循环(EGR)和进气节流阀(TVA)的柴油机进气歧管压力、涡前压力和EGR率多目标跟踪控制问题,提出一种权重系数自学习的扩张状态模型预测控制(MPC)算法.首先,基于柴油机的简化运行机理,提出了三入三出线性变参数预测模型;其次,为补偿因模型与发动机之间偏差造成MPC性能劣化问题,将其等效为3个控制通道的总扰动,并采用扩张状态观测器(ESO)进行快速在线观测和补偿;最后,引入极值搜索算法,对MPC权重系数进行缓慢优化整定,仿真结果表明:在1800 r/min油门开度以最大开度10%的步长向上和向下连续阶跃工况下,对比涡前压力控制,瞬态上升时间相同时本算法超调量比较参数全局优化的比例-积分-微分控制(PID)算法降低30.7%.台架试验中,世界统一瞬态循环(WHTC)动态驾驶循环中进气歧管压力和EGR率跟踪误差IAE(绝对误差积分)值比较传统PID算法分别降低28.3%、17.5%.

Regarding the multi-objective tracking control problem of the intake manifold pressure,pre-turbine pressure and EGR rate of a diesel engine equipped with a variable geometry turbocharger(VGT),high-pressure exhaust gas recirculation(EGR)and throttle valve actuator(TVA),a model predictive control(MPC)algorithm with self-learning weight coefficients was proposed.Firstly,based on the simplified operating mechanism of the diesel engine,a three-input and three-output linear time-varying prediction model for MPC was proposed.Then,in order to compensate for the performance degradation of MPC caused by the deviation between the model and the engine,it was equivalent to the total disturbance of three control channels,and an extended state observer(ESO)was used for fast online observation and compensation.Finally,an extreme search algorithm was introduced to slowly optimize and adjust the weight coefficients of MPC.The results simulation show that under continuous throttle up and down with a maximum opening step of 10%at a speed of 1800 r/min in the same transient rise time,the overshoot of this algorithm is 30.7%lower than that of proportion integration differentiation(PID)algorithm with global optimization parameters in pre-turbine pressure.In bench test,the tracking error IAE(absolute error integral)value of intake manifold pressure and the EGR rate in WHTC dynamic driving cycle is reduced by 28.3%and 17.5%respectively,compared with traditional PID algorithm.