Can quantized MPC optimize platooning?
Fully Distributed and Quantized Algorithm for MPC-based Autonomous Vehicle Platooning Optimization
This paper proposes a distributed algorithm for optimizing the control of a platoon of autonomous vehicles using Model Predictive Control (MPC). The algorithm allows each vehicle to make decisions locally by communicating with its neighbors over a network with limited bandwidth, using log-scale quantization to compress the exchanged data. This approach enhances resilience and efficiency compared to centralized control.
Key points for LLM-based multi-agent systems: The distributed optimization framework, focusing on local computation and limited communication, is highly relevant. The use of log-scale quantization offers a potential strategy for managing communication costs in LLM-based multi-agent applications where large language models exchange extensive data. The principles of cooperative control demonstrated in vehicle platooning translate to collaborative task completion in multi-agent LLM systems.