Trajectory tracking control method for unmanned autonomous vehicle

Abstract

The invention discloses a trajectory tracking control method for an unmanned autonomous vehicle. The trajectory tracking control method is specifically implemented according to the following steps: establishing a two-degree-of-freedom kinematics model of the unmanned autonomous vehicle; constructing a trajectory tracking controller based on nonlinear model predictive control, and adding a single-step Euler method into a predictive model of the controller; designing a constraint equation of the nonlinear model predictive controller, and establishing a target function based on the minimum difference value between the control trajectory output of the nonlinear model predictive controller and the reference trajectory; and solving the input of the unmanned autonomous vehicle according to the minimum objective function. A proposed nonlinear model predictive controller can track a reference trajectory more quickly, and has a smaller fluctuation error and a smoother trend in the tracking process. The control method provided by the invention can effectively improve the driving stability and safety of the vehicle, and has great significance for trajectory tracking control of the unmanned vehicle.

Description

technical field [0001] The invention belongs to the technical field of trajectory tracking control of unmanned vehicles, and in particular relates to a trajectory tracking control method of unmanned autonomous vehicles. Background technique [0002] In recent years, with the rapid development and wide application of unmanned driving technology, the key technologies related to unmanned driving have attracted the favor and research of a large number of domestic and foreign scholars. On the one hand, the research on unmanned driving technology provides a theoretical basis for the development of modern intelligent vehicles; Provide technical support for the development and application of [0003] The PID control algorithm is widely used in the automotive industry, but since the internal parameters (speed, yaw angle, steering angle) and external parameters (road curvature, drag coefficient) of the driving process of the driverless car are constantly changing, the adjustment para...

AI Technical Summary

Problems solved by technology

[0003] PID control algorithm is widely used in the automotive industry, but due to the internal parameters (speed, yaw angle, steering angle) and external parameters of the driverless car driving process (Road curvature, drag coefficient) are always changing, so the process of adjusting parameters will be very complicated, the adaptability will be poor, and it is difficult to be widely used in actual unmanned vehicles

For example, some emerging algorithms, such as reinforcement learning algorithms and adaptive neural network algorithms, mostly use pre-set road conditions or obtain training data sets in advance to study the trajectory of unmanned vehicles, which simply cannot satisfy Implementation Control Requirements for Autonomous Vehicles

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