Energy-Optimal Spatial Iterative Learning within a Virtual Tube

2026-06-30

AI Summary

This study addresses the limited flight endurance and operational range of unmanned aerial vehicles (UAVs) imposed by constrained onboard energy resources. Existing approaches typically rely on accurate system models and incur substantial computational overhead. To overcome these limitations, this work proposes a model-free online spatial iterative learning control framework that directly optimizes flight trajectories at the waypoint level under virtual tube constraints to minimize energy consumption, without requiring explicit dynamics or energy models. Notably, this method achieves energy-optimal online iterative learning without any prior knowledge of the system model, with a computational complexity of only O(n), drastically reducing computational demands. Simulations and real-world flight experiments demonstrate that the proposed approach is 50–60 times faster than the model-based IPOPT benchmark while significantly improving energy efficiency.

Abstract

Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned aerial vehicles (UAVs) are severely constrained. Although energy-efficient trajectory planning and control have been widely studied, most existing approaches rely on accurate system models and computationally expensive optimization procedures. This paper proposes a model-free online iterative learning (IL) framework to minimize energy consumption. Without requiring explicit models of UAV dynamics or energy consumption, the proposed method improves energy efficiency while maintaining a low computational cost. The per-iteration computational complexity is O(n), where n denotes the number of path points. In the tested cases, the proposed method is approximately 50--60 times faster than the model-based IPOPT benchmark. Simulation results and real-world flight experiments across multiple UAV platforms validate the effectiveness, computational efficiency, and practical applicability of the proposed approach.

Problem

• energy optimization
• unmanned aerial vehicles
• trajectory planning
• computational efficiency
• battery endurance

Innovation

• model-free iterative learning
• energy-optimal trajectory
• low computational complexity
• UAV energy efficiency
• online learning control