Learning Agent-based Model Predictive Control for Holistic Vehicle Performance

Analysis

Why This Paper Matters

Agent-based model predictive control (AMPC) has emerged as a promising distributed control scheme for multi-agent systems, where agents collaborate to optimize holistic performance. However, its effectiveness hinges on accurate predictions of all agents' contributions, an assumption that is often unrealistic in real-world deployments. This paper addresses this critical gap by proposing Learning Agent-based MPC (LAMPC), a hybrid control scheme that combines model-based AMPC with data-driven learning to handle unknown agent contributions. By leveraging Gaussian process regression (GPR) to predict these unknowns, LAMPC bridges the divide between theoretical optimality and practical feasibility, making it highly relevant for applications like autonomous vehicles where safety and performance are paramount.

The significance of this work lies in its potential to enable robust and adaptive control in multi-agent settings without requiring complete knowledge of all agents. This is particularly important in dynamic environments where agent behaviors may be uncertain or changing. By integrating learning into the control loop, LAMPC can adapt online, improving performance over time. Moreover, the stochastic framework with soft chance constraints ensures safety even when predictions are uncertain, a crucial feature for real-world deployment. This research thus represents a step forward in making advanced control techniques viable for complex, real-world multi-agent systems.

Technical Contributions

• Hybrid Control Architecture: LAMPC seamlessly integrates model-based AMPC with data-based learning, using GPR to predict unknown contributions of agents. This hybrid approach retains the strengths of MPC while adding adaptability.
• Online Data Management and Multi-step Prediction: A novel online data management strategy enhances GPR's learning capability, and a multi-step prediction mechanism extends predictions along the MPC horizon, improving foresight and control accuracy.
• Stochastic Safety Framework: The paper formulates soft chance constraints based on GPR prediction variance, guaranteeing constraint satisfaction with high probability even in less-learned scenarios. This provides a principled way to handle uncertainty.
• Real-time Efficiency and Flexibility: The proposed scheme is designed for real-time implementation and is flexible to any control agent topology, making it applicable to various multi-agent configurations.

Results

The paper reports both simulation and experimental results demonstrating LAMPC's superiority over traditional AMPC. In well-learned scenarios, LAMPC achieves higher tracking performance, indicating better holistic vehicle performance. In less-learned scenarios, LAMPC consistently guarantees constraint satisfaction, whereas AMPC may fail. The results highlight the effectiveness of the learning component in improving control accuracy and safety. Additionally, the computational efficiency of LAMPC is validated, showing it is suitable for real-time applications. While specific metrics are not provided in the abstract, the qualitative comparisons strongly support the method's advantages.

Significance

LAMPC's significance extends beyond vehicle control to any multi-agent system requiring coordinated control under uncertainty. By combining model-based control with data-driven learning, it offers a blueprint for developing safe, adaptive, and high-performance control systems. This work could influence future research in reinforcement learning, control theory, and autonomous systems, particularly in domains like robotics, transportation, and smart grids. The integration of GPR with MPC also opens avenues for probabilistic safety guarantees in learning-enabled control, a critical area for deploying AI in safety-critical applications.