プラグアンドプレイ車両制御のための多目的エージェントベースモデル予測制御
Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control
車両制御における複数の目的を協調させるため、ADMMを応用した多目的エージェントベースMPCを提案し、実車実験で有効性を検証した。
詳しい要約
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著者: Jiaming Zhong, Ladan Khoshnevisan, Shucheng Huang, Mohammad Pirani, Yash Vardhan Pant, Amir Khajepour
分類: cs.RO
原文アブストラクト
Functional integration is a growing trend in vehicle control, often involving the coordination of multiple controllers to achieve various objectives simultaneously. The need for flexibility and reliability has led to a "plug-and-play" approach in control system design, which presents challenges for traditional integrated model predictive control (MPC). Agent-based model predictive control (AMPC) has recently emerged as a distributed solution that treats controllers as agents, creating a collaborative framework among them to reach a common goal. However, this approach struggles to manage distributed conflicting objectives when agents are coupled or interdependent. To address this, we propose a novel, practical distributed control scheme called multi-objective AMPC, which adapts the alternating direction method of multipliers (ADMM) into a general control strategy that approximates global optimization while decoupling objectives. We systematically develop three formulations that maintain convergence while addressing control regularization and inequality constraints, applying them to complex vehicle control systems for the first time. The proposed method has been tested on two vehicle control scenarios with a multi-objective topology. Different formulations are compared through simulations, and the most computationally efficient one was implemented on an electric vehicle for real-world evaluations. The results demonstrate that the proposed multi-objective AMPC can converge approximately to the same global optimum as integrated MPC with greater flexibility and the potential to reduce computational costs.
