Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control
arXiv:2606.08714v1 Announce Type: cross Abstract: Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under-actuation. Tilt-rotor configurations overcome this limitation by enabling full actuation. This paper investigates neural-network-based control strategies for a fully actuated tilt-rotor system with four thrust-vectoring inputs. Our work is structured in two parts. First, we deliberately present a negative result by evaluating a direct input-output control approach. In this method, multilayer perc
The paper leverages recent advancements in neural networks to address the complex control challenges of uncrewed aerial vehicles, indicating an accelerating trend towards AI-driven autonomous systems in physical domains.
This research demonstrates a practical application of AI in enhancing the capabilities and robustness of tilt-rotor systems, directly impacting defence, logistics, and surveillance sectors, and pushing the boundaries of autonomous navigation.
The deployment of hybrid neural network controllers for highly unstable systems like tilt-rotors promises a significant leap in performance and reliability beyond conventional control methods, broadening the scope for their application.
- · Defense contractors
- · Precision agriculture companies
- · AI/ML research labs
- · Logistics and delivery services
- · Manufacturers of conventional multirotors
- · Developers of non-AI based control systems
Increased adoption of AI-controlled aerial platforms in various challenging environments.
Accelerated development of more complex and versatile autonomous systems with reduced human oversight.
Potential for new ethical and regulatory frameworks concerning highly autonomous machines in public and sensitive operations.
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