Composite Learning-Based Inverse Optimal Fault-Tolerant Control for Hierarchy-Structured Unmanned Helicopters

• Published in: Drones (Basel) Vol. 9; no. 6; p. 391
• Main Authors: Liu, Qingyi, Zhang, Ke, Jiang, Bin, Tan, Yushun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: composite learning; Control systems; Convexity; Design; Drone aircraft; Energy consumption; fault estimation; Fault tolerance; formation-containment; Helicopter control; Helicopters; inverse optimal fault-tolerant control; Machine learning; Partial differential equations; Sensors; Stability analysis; Subsystems; Unmanned helicopters; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones9060391

This article investigates the inverse optimal fault-tolerant formation-containment control problem for a group of unmanned helicopters, where the leaders form a desired formation pattern under the guidance of a virtual leader while the followers move toward the convex hull established by leaders. To facilitate control design and stability analysis, each helicopter’s dynamics are separated into an outer-loop (position) and an inner-loop (attitude) subsystem by exploiting their multi-time-scale characteristics. Next, the serial-parallel estimation model, designed to account for prediction error, is developed. On this foundation, the composite updating law for network weights is derived. Using these intelligent approximations, a fault estimation observer is constructed. The estimated fault information is further incorporated into the inverse optimal fault-tolerant control framework that avoids tackling either the Hamilton–Jacobi–Bellman or Hamilton–Jacobi–Issacs equation. Finally, simulation results are presented to demonstrate the superior control performance and accuracy of the proposed method.