Co-DMPC-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, controller, and storage medium

Patent metadata

Field	Value
Publication number	US-12509115-B1
Application number	US-202419113503-A
Country	US
Kind code	B1
Filing date	Jul 2, 2024
Priority date	Jun 27, 2024
Publication date	Dec 30, 2025
Grant date	Dec 30, 2025

Abstract

The present disclosure provides a cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, a controller, and a storage medium. A distributed state-space equation with state coupling and control input coupling characteristics is established. Meanings and transformation methods of predicted trajectories, assumed trajectories, and optimal trajectories of the states and control inputs are designed, providing a communication basis for information exchange between the agents. In order to coordinate the global performance indexes of a vehicle, a local agent optimization problem considering cost coupling is established, and the influence of the cooperative relationship on the control effect is quantitatively analyzed through adaptive weight coefficients. A method of performing a plurality of iterations within a unit sampling time is adopted, and iteration errors are utilized to enable the controller to achieve a balance between solution accuracy and efficiency.

First claim

The invention claimed is:

1 . A cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, comprising: S1: building a two-degree-of-freedom vehicle model with four-wheel steering and direct yaw moment control (DYC); S2: establishing a distributed state-space equation, wherein a centralized state-space equation of a vehicle is divided into two agents, namely, a steering agent and a DYC agent according to different control variables; as for the steering agent, the distributed state-space equation has control variables of a front wheel steering angle δ f and a rear wheel steering angle δ r and state variables of a lateral velocity v y and a vehicle lateral displacement Y; as for the DYC agent, the distributed state-space equation has a control variable of a direct yaw moment M z and tracked state variables of a yaw rate γ and a yaw angle φ S3: building a cooperative control framework, comprising: an input layer, wherein the two-degree-of-freedom vehicle model computes a reference yaw rate γ ref and a reference lateral velocity v ref according to deviation between a current state of the vehicle and a target path; a cooperative control layer, wherein a centralized chassis control system is decomposed into a distributed control system consisting of a plurality of controllers, to achieve tracking of expected targets; through the distributed state-space equation established in the S2, a Co-DMPC algorithm is employed to select a cost function considering global performance; an iterative update method is used in a unit sampling time, enabling agents to independently compute local optimization and exchange computation results multiple times, and an entire chassis multi-agent system obtains a global optimal solution; a moment distribution layer, wherein driving/braking torques of four wheels are optimally distributed based on a total driving force computed by a proportional-integral-derivative (PID) speed controller and a direct yaw moment computed by the cooperative control layer as constraints; and an actuation layer, wherein the control variables are applied to the four wheels through a steering actuator and a hub motor to achieve accurate control of the vehicle; S4: establishing an information transmission protocol, wherein a same prediction time domain N p and a same control time domain N c are used in all the agents; as for an agent i, in a control time domain [t, t+N c ], a control variable at a time point t+k is predicted at a time point t as u i (k|t), and following three sets of control trajectories are defined: u i p (k|t): predicted control trajectory; u i *(k|t): optimal control trajectory; u i a (k|t): assumed control trajectory; wherein u i p (k|t) represents a control variable at a future time point predicted based on the current state and a control variable at a previous time point; u i *(k|t) represents an optimal solution obtained after solving local optimization problems; u i a (k|t) represents an assumed control variable transmitted by the agent i to a neighbor agent and is obtained by shifting a predicted control variable by one sampling period; in a prediction time domain [t, t+N p ], a state variable at the time point t+k is predicted at the time point t as x i (k|t), and following three sets of state trajectories are defined: x i p (k|t): predicted state trajectory; x i *(k|t): optimal state trajectory; x i a (k|t): assumed state trajectory; the agent i uses, at the time point t, a predicted state at the future time point and an assumed state x j p (k|t) and an assumed control u j a (k|t) from the neighbor agent to solve a distributed optimization problem; when k=0, x i p (0|t)=x i (t) and x j p (0|t)=x j (t), wherein x i (t) and x j (t) are directly measured current states of the vehicle; after obtaining an optimal state x i *(k|t) and an optimal control u i *(k|t), a first u i *(0|t) of a control trajectory is applied to actual control; a single-step recursion is performed on the optimal state trajectory and the optimal control trajectory to obtain assumed trajectories as follows: u i a (k−1|t+1)=u i *(k|t), k∈[1, N c −1]
x i a ( k− 1| t+ 1)= x i *( k|t ), k∈[ 1, N p ]   (1)

wherein a length of the control tr to complete a last term, u i a (N c −1|t+1)=0; and a state of the vehicle not under control at a next time point is x i a (N p |t+1)=Ax i a (N p −1|t+1)=Ax i *(N p |t); and S5: controlling the four wheels through the steering actuator and the hub motor using the control variables.

2 . The Co-DMPC-based chassis MAS cooperative control method for the autonomous vehicles according to claim 1 , wherein the two-degree-of-freedom vehicle model in the S1 is:

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Assignees

• Univ Jiangsu

Inventors

• Cai Yingfeng
• Li Yuxing
• Lian Yubo
• Dong Zhaozhi
• Sun Xiaoqiang
• Chen Long

Classifications

• B60W2720/14

  Yaw · CPC title

• B60W50/0097

  Predicting future conditions · CPC title

• B60W60/001 Primary

  Planning or execution of driving tasks · CPC title

• B60W2050/0011

  Proportional Integral Differential [PID] controller · CPC title

• B60W2050/0034

  Multiple-track, 2D vehicle model, e.g. four-wheel model · CPC title