arXiv:2512.13356v2 Announce Type: replace-cross Abstract: This paper proposes a reinforcement learning (RL) framework for controlling and stabilizing the Twin Rotor Aerodynamic System (TRAS) at specific pitch and azimuth angles and tracking a given trajectory. The complex dynamics and non-linear characteristics of the TRAS make it challenging to control using traditional control algorithms. However, recent developments in RL have attracted interest due to their potential applications in the control of multirotors. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm was used in
The continuous advancements in reinforcement learning algorithms are enabling new applications previously difficult to achieve with traditional control methods, making this a timely development.
This development showcases the increasing viability of advanced AI for complex robotic control, moving towards more autonomous and adaptable systems.
The application of TD3 to a challenging system like TRAS demonstrates a growing capability for AI to manage highly dynamic and non-linear physical systems, expanding the frontiers of automated control.
- · AI algorithm developers
- · Robotics manufacturers
- · Aerospace & defense industry
- · Logistics and delivery sectors
- · Manufacturers relying solely on traditional control systems
- · Industries resistant to AI integration
Improved performance and stability in complex robotic systems, reducing operational costs and increasing efficiency.
Accelerated development and adoption of AI-driven autonomous systems across various industries, from manufacturing to drone delivery.
Potential for new regulatory frameworks and ethical considerations as AI takes on more critical control functions in physical world applications.
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Read at arXiv cs.LG