Pilotless automobile longitudinal control method

Abstract

The invention provides a pilotless automobile longitudinal control method which comprises the following steps: S1, establishing a discrete kinematics model of an automobile longitudinal control system, and taking a state expected acceleration as an input control quantity of the discrete kinematics model of the longitudinal control system; S2, creating a linear TD, arranging a transition process ofa reference speed signal and a reference acceleration signal through the linear TD, and converting the stepped reference speed signal and reference acceleration signal into a continuous reference signal sequence; and S3, creating an MPC, connecting the MPC with the linear TD, obtaining an optimal control sequence through the discrete kinematics model based on a reference sequence number sequenceinput by the linear TD under the condition of existence of a control quantity constraint and a control quantity increment constraint, and finishing longitudinal control of the pilotless automobile. The method has the advantages of short adjustment time and small steady-state error.

Description

technical field [0001] The invention belongs to the field of unmanned driving control, and specifically relates to a longitudinal control method of an unmanned vehicle combined with a linear tracking differentiator and a model predictive control algorithm. Background technique [0002] In recent years, electric vehicles have been widely used in various scenarios, such as driverless cars, smart cities, etc. Compared with traditional internal combustion engine vehicles, electric vehicles have the advantages of high efficiency, energy saving, and environmental protection. From an ideal situation, electric vehicles will gradually replace traditional vehicles, which is conducive to the realization of zero vehicle exhaust emissions. Environmental perception, motion planning, and motion control are the three core technologies in unmanned driving technology. Among them, due to the influence of factors such as complex road conditions, diverse driving environments, and physical cons...

AI Technical Summary

Problems solved by technology

Although the research on MPC in recent years has a large number of experimental verifications, the analysis of the iterative feasibility and stability of MPC is not reflected in these works, resulting in the lack of theoretical support for these results

In addition, in actual control, unreliable reference signals may cause MPC to lose the feasible solution at the initial moment when the terminal constraints are relatively conservative. This situation cannot be solved by tuning the MPC parameters

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