Distributed Chassis Control for Coupled Autonomous Vehicle Subsystems
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Solution Overview
Problem
Existing centralized control methods for autonomous vehicle chassis systems face challenges with increased complexity, computational burden, and scalability issues, while decentralized methods lack effective coordination of subsystems under strong coupling conditions.
Innovation Solution
A distributed model predictive control (Co-DMPC) architecture based on a multi-agent system (MAS) is employed, iteratively solving state and control trajectories across agents to enhance control accuracy and reduce computing time, with a cooperative control framework that optimizes global performance through iterative updates and information exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized control methods are used to coordinate complex coupling problems among subsystems, then control coordination capability is improved, but device complexity and computational burden increase
Solution Approach 1:
The centralized controller is segmented into multiple distributed controllers, each responsible for specific subsystems. The vehicle chassis control system is divided into steering control module, driving/braking control module, and suspension control module, with each module having its own controller that processes local information independently while maintaining system-wide coordination through information sharing.
Solution Approach 2:
The control architecture transitions from a single-dimensional centralized structure to a multi-dimensional distributed structure. Controllers operate at multiple levels: individual subsystem level, module level (steering, driving/braking, suspension), and system level, creating a hierarchical dimensionality that reduces complexity at each level while maintaining overall coordination.
2Device complexity
If decentralized control methods are used to reduce system complexity, then device complexity is reduced, but ability to process coupling state information deteriorates
Solution Approach 1:
Distributed controllers implement feedback mechanisms where each controller monitors its local subsystem state and receives state information from other subsystems. The controllers continuously adjust control inputs based on feedback from path tracking error, vehicle dynamics state, and coordinated control requirements, ensuring that coupling relationships are properly managed despite the distributed architecture.
Solution Approach 2:
While maintaining independence of individual controllers, the system merges information processing capabilities through shared state information. Controllers combine local subsystem data with global vehicle state information to make coordinated decisions, effectively merging the computational advantages of decentralization with the coordination benefits of centralization.
3Measurement precision
If centralized control methods are used to achieve optimal system control, then control accuracy is improved, but computing time increases
Solution Approach 1:
The computationally intensive centralized optimization problem is segmented into multiple smaller sub-problems, each handled by individual distributed controllers. Each controller solves a localized optimal control problem for its subsystem based on current state information, significantly reducing computational time while maintaining overall system optimality through coordinated information exchange.
Solution Approach 2:
Controllers perform preliminary calculations of local optimal control inputs based on predicted future states and current system conditions. By pre-computing control actions within each subsystem and then coordinating these preliminary solutions through information sharing, the system achieves near-optimal global control with reduced computational burden compared to solving the full centralized problem.
Data Source
AI summary
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.