为重型机械电控系统提供安全可靠的智能控制框架，支持未来升级。
Robust Deep Learning Control with Guaranteed Performance for Safe and Reliable Robotization in Heavy-Duty Machinery
- 采用模块化设计，兼容不同动力源和执行机构，简化控制开发。
- 在故障与不确定性下仍保证性能稳定，平衡鲁棒性与响应速度。
- 融合可解释的AI策略，满足国际安全标准，适合高风险场景应用。
当前重型移动机械（HDMMs）面临双重转型：从柴油液压驱动转向清洁电驱动以应对气候目标，从人工监管向更高自主性演进。尽管先进人工智能可提升自主性，但严苛的安全要求限制了其在HDMM中的应用，机器仍依赖人工监督。本文提出一种控制框架，（1）通过通用模块化方法简化电驱HDMM的控制设计，实现能源类型无关且支持未来修改；（2）构建分层控制策略，部分集成AI同时确保安全定义的性能与稳定性。研究围绕五个核心问题展开，涵盖多体系统强稳定性控制、不确定性和故障下的性能保持，以及黑箱学习策略的可解释性与验证方法。框架在三类案例中验证，涵盖不同执行器与工况，包括重型移动机器人和机械臂。成果发表于五篇同行评审论文及一篇未发表手稿，推动非线性控制与机器人学发展，支持上述双重转型。
原文摘要 · Abstract (English)
Today's heavy-duty mobile machines (HDMMs) face two transitions: from diesel-hydraulic actuation to clean electric systems driven by climate goals, and from human supervision toward greater autonomy. Diesel-hydraulic systems have long dominated, so full electrification, via direct replacement or redesign, raises major technical and economic challenges. Although advanced artificial intelligence (AI) could enable higher autonomy, adoption in HDMMs is limited by strict safety requirements, and these machines still rely heavily on human supervision. This dissertation develops a control framework that (1) simplifies control design for electrified HDMMs through a generic modular approach that is energy-source independent and supports future modifications, and (2) defines hierarchical control policies that partially integrate AI while guaranteeing safety-defined performance and stability. Five research questions align with three lines of investigation: a generic robust control strategy for multi-body HDMMs with strong stability across actuation types and energy sources; control solutions that keep strict performance under uncertainty and faults while balancing robustness and responsiveness; and methods to interpret and trust black-box learning strategies so they can be integrated stably and verified against international safety standards. The framework is validated in three case studies spanning different actuators and conditions, covering heavy-duty mobile robots and robotic manipulators. Results appear in five peer-reviewed publications and one unpublished manuscript, advancing nonlinear control and robotics and supporting both transitions.
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