🤖 AI Summary
Heavy-duty mobile machines (HDMMs) face dual challenges in electrification—constrained by techno-economic limitations—and high-level autonomy—hindered by stringent functional safety requirements. To address these, this project proposes a hierarchical intelligent control framework integrating multibody dynamics modeling, uncertainty suppression, and fault-tolerant mechanisms. Key contributions include: (1) a source-agnostic, modular robust control architecture; and (2) a verifiable AI-control co-design paradigm, enabling formal co-verification of deep reinforcement learning policies with ISO 13849 functional safety standards. The approach combines nonlinear robust control, adaptive observers, policy distillation, and multiphysics co-simulation, validated via hardware-in-the-loop testing across three HDMM platforms. Results demonstrate significant improvements in system response robustness and fault recovery capability. Outcomes are disseminated in five peer-reviewed publications and support industry-wide safe autonomous upgrading.
📝 Abstract
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.