Unmanned vehicle path following method based on event-triggering model prediction control

Abstract

The invention relates to an unmanned vehicle path following method based on event-triggering model prediction control. The method comprises the steps that a nonlinear kinematic model of a vehicle is established; unmanned vehicle kinematic global coordinate position information is collected and transmitted to a controller of the vehicle; an event triggering mechanism is designed, an optimization problem is solved to perform redundancy elimination processing, and whether a control strategy of the vehicle is updated or not is decided; and a model prediction controller solves an optimal control strategy by use of the position information in combination with state constraint and input constraint in a vehicle global coordinate system. According to the method, the event triggering mechanism is introduced, and it can be guaranteed that following to a reference path is guaranteed while the calculation amount is reduced; and compared with traditional periodical sampling control, an action is done only at the occurring moment of a determined event, for example, an error exceeds a threshold or a specific moment is reached, according to the event triggering mechanism, path following control isrealized, and meanwhile the calculation amount is greatly reduced.

Description

technical field [0001] The present invention relates to the technical field of automobile driving adaptive cruise system, in particular to a motion control system of automobile adaptive cruise system, and in particular to an unmanned vehicle path following method based on event-triggered model predictive control. Background technique [0002] Unmanned driving technology senses the road environment through the on-board sensor system, automatically plans the driving route and controls the vehicle to reach the predetermined target. Control the steering and speed of the vehicle so that the vehicle can move safely and reliably on the road. Integrating many technologies such as automatic control, architecture, artificial intelligence, and visual computing, it is the product of highly developed computer science, pattern recognition, and intelligent control technologies. [0003] The core of adaptive cruise system unmanned driving technology. Path-following control is the basic pr...

AI Technical Summary

Problems solved by technology

[0004] Model predictive control uses rolling optimization and feedback correction methods, which can better solve optimization problems with constraints, and the predictive model can be combined with the controlled object itself, no matter whether the controlled object is a linear model or a nonlinear model, it can be predicted To achieve more precise control, it is widely used in the field of unmanned driving. The main disadvantage of model predictive control is the computational burden of online optimization

The existing vehicle hardware level is difficult to guarantee the solution of the optimization problem

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