Abstract: 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 1 800 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.