Learning Hierarchical Control Systems for Autonomous Systems with Energy Constraints
arxiv(2024)
摘要
This paper focuses on the design of hierarchical control architectures for
autonomous systems with energy constraints. We focus on systems where energy
storage limitations and slow recharge rates drastically affect the way the
autonomous systems are operated. Using examples from space robotics and public
transportation, we motivate the need for formally designed learning
hierarchical control systems. We propose a learning control architecture which
incorporates learning mechanisms at various levels of the control hierarchy to
improve performance and resource utilization. The proposed hierarchical control
scheme relies on high-level energy-aware task planning and assignment,
complemented by a low-level predictive control mechanism responsible for the
autonomous execution of tasks, including motion control and energy management.
Simulation examples show the benefits and the limitations of the proposed
architecture when learning is used to obtain a more energy-efficient task
allocation.
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