Data-driven modeling based sd-arx-mpc control method for dct vehicles

Abstract

The present invention relates to automobile field, be specifically related to a kind of DCT vehicle starting SD-ARX-MPC control method based on data-driven modeling, comprise the following steps: 1) establish SD-ARX structure model; 2) convert SD-ARX structure model into Multi-step prediction model, the theoretical starting output state parameters of the predicted DCT vehicle in the time domain during the driving process; 3) Construct a multi-objective optimization function, use the particle swarm optimization algorithm to solve the multi-objective optimization function, and calculate the DCT vehicle in the driving process In the process, the starting control quantity in the time domain is predicted; 4) The starting control quantity is used to control the impact and sliding work in the DCT vehicle prediction time domain, and the actual starting output state parameters in the DCT vehicle prediction time domain are obtained; 5) The threshold value is set, and the theoretical Compare the error value of the start output state parameter with the actual start output state parameter and the threshold value: if the error value is less than the threshold value, the SD‑ARX structural model is valid; if the error value is greater than or equal to the threshold value, the SD‑ARX structural model is invalid, and repeat steps 1)~ Step 5).

Description

technical field [0001] The invention relates to the field of automobiles, in particular to a data-driven modeling-based SD-ARX-MPC control method for starting a DCT vehicle. Background technique [0002] In recent years, vehicles with dual-clutch automatic transmissions (referred to as DCT vehicles) have attracted widespread attention in the global automotive industry due to their high transmission efficiency and smoothness of vehicles, and launch control is one of the key and difficult points of DCT vehicles: During the start process, there is a need to reduce impact and friction losses, but the two are somewhat opposed to each other. In addition, the driver's starting intention is a factor that must be considered in the starting control. In view of these problems, it is essential to design an effective launch control strategy. [0003] At present, there are many studies on the starting control strategy of DCT vehicles, mainly including intelligent control strategies base...

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Problems solved by technology

[0004] 1) The intelligent control strategy based on fuzzy control can well reflect the driver's starting intention and has good robustness, but its control effect is completely dependent on the formulation of fuzzy rules, and cannot realize the dynamics of the clutch engagement process. optimized control

[0005] 2) The model-based optimal control strategy can realize the global or local optimization of the start-up process. However, the DCT start-up process is complex and time-delayed, and it is difficult and time-consuming to establish an accurate and efficient physical model. and aging after long-term service, the physical model will also produce certain modeling errors, which will affect the control effect

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