Adaptive model prediction control algorithm for improving steering and braking cooperative control

Abstract

An adaptive model prediction control algorithm for improving steering and braking cooperative control is characterized in that a reference model, a tire data processor, an MPC controller, a braking force distribution module and a CarSim automobile model are adopted in the method; the reference model is used for determining the expected automobile yaw velocity and the expected side slip angle; thetire data processor is used for determining the slip angle, the lateral force and the lateral force gradient of each tire; the CarSim automobile model is used for outputting actual motion state information of an automobile, wherein the actual motion state information comprises the automobile longitudinal velocity, the yaw velocity, the side slip angle and the road adhesion coefficient; the MPC controller obtains the front wheel turning angle and the compensation yawing moment of the automobile through optimization solution according to the expected automobile yaw velocity, the expected side slip angle and the actual motion state information of the automobile; and the front wheel turning angle is directly output to the CarSim automobile model, the compensation yawing moment is output to thebraking force distribution module, the braking moment of four wheels is determined through the braking force distribution module and output to the CarSim automobile model, and stability control is achieved.

Description

Technical field: [0001] The invention relates to the field of vehicle stability control, in particular to an adaptive model predictive control algorithm for improving steering and braking cooperative control. Background technique: [0002] With the continuous development of vehicle chassis dynamics control, integrated control has become the direction of future development, active front wheel steering and differential braking cooperative control of vehicles to achieve stability control has been widely studied. At present, the control methods involved in the field of vehicle stability control mainly include robust control, neural network control, and model predictive control (Model Predictive Control, MPC), among which model predictive control can better handle multi-objective tasks and system constraints. It has been widely used in the field of automobile stability control. [0003] According to different prediction models and optimization methods used, MPC can be divided in...

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Problems solved by technology

[0004] In order to solve the problem that the existing linear time-varying MPC method cannot reflect the nonlinear dynamic characteristics of the vehicle in the rolling prediction process, resulting in poor control effect of the control system under extreme conditions

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