Transverse control method of pilotless automobile

Abstract

The invention provides a transverse control method for a pilotless automobile; the method comprises the steps: a vehicle posture collection module collects the state and positioning information of a vehicle, an error calculation module calculates a trajectory tracking error, an LQR controller which does not consider the influence of road curvature obtains a feedback control signal according to the tracking error, and the feedback control signal is sent to a control module; the feedforward controller considering the influence of the road curvature obtains a feedforward control signal according to the tracking error of the error calculation module, initializes Q and R matrix parameters of the LQR controller and PID controller parameters, and uses an adaptive seagull algorithm to find the optimal parameters of the Q and R matrixes and the PID controller so as to obtain a final output signal of the LQR controller; and a front wheel steering command signal is obtained by the sum of a final output signal of the LQR controller and an output signal of the PID embedded feedforward controller. According to the method, the optimal solution of the Q and R matrixes in the feasible region can be quickly found, and the control effect of the controller is fully exerted to realize the transverse control of the pilotless automobile.

Description

technical field [0001] The invention relates to the technical field of lateral control of unmanned vehicles, in particular to a lateral control method of unmanned vehicles. Background technique [0002] In driverless cars, the upper controller performs path planning according to the real-time conditions of the driving road, and then plans the tracking trajectory according to the current driving state of the vehicle. The purpose of the lateral controller is to control the front wheel angle of the driverless vehicle so that The vehicle can track the preset trajectory smoothly and accurately. Common vehicle lateral control algorithms include PID controller, sliding mode controller, linear quadratic regulator (LQR), model prediction and deep learning, etc. Among the above various control algorithms, the LQR controller is a relatively widely used control algorithm, and the parameters of the Q and R matrices in LQR have a great influence on the control effect of the entire contro...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the invention is: Aiming at the technical problems existing in the prior art, it provides a lateral control method for unmanned vehicles, which can solve the problems in the LQR controller. Q, R matrix parameters are difficult to select, and at the same time, it can overcome the problem of sudden changes in the control amount of the feedforward controller when the road curvature changes suddenly, and improve the stability and accuracy of the driverless car in tracking the trajectory.

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