Reinforcement learning is an appealing approach for adaptive, fault-tolerant flight control, but is generally plagued by its need for accurate system models and lengthy offline training phases. The novel Incremental Dual Heuristic Programming (IDHP) method removes these dependencies by using an online-identified local system model. A recent implementation has shown to be capable of reliably learning near-optimal control policies for a fixed-wing aircraft in cruise by using outer loop PID and inner-loop IDHP rate controllers. However, fixed wing aircraft are inherently stable, enabling a trade-off between learning speed and learning stability which is not trivially extended to a physically unstable system. This paper presents an implementation of IDHP for control of a non-linear, six-degree-of-freedom simulation of an MBB Bo-105 helicopter. The proposed system uses two separate IDHP controllers for direct pitch angle and altitude control combined with outer loop and lateral PID controllers. After a short online training phase, the agent is shown to be able to fly a modified ADS-33 acceleration-deceleration manoeuvre as well as a one-engine-inoperative continued landing with high success rates.
|Name||AIAA Scitech 2021 Forum|
|Conference||AIAA Scitech 2021 Forum|
|Period||11/01/21 → 21/01/21|