Fault Tolerant Control combining Reinforcement Learning and Model-based Control

Bhan, Luke; Quiñones-Grueiro, Marcos; Biswas, Gautam

doi:10.1109/systol52990.2021.9595275

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Kahn et al [34] and Bhan et al [56] worked on failure avoidance and on compensating for failures occurring during flights. In [34], a Policy Learning using Adaptive Trajectory Optimization (PLATO) algorithm, a continuous, reset-free RL algorithm, was developed.…”

Section: Policy-search-based Algorithmsmentioning

confidence: 99%

“…It was shown that good long-horizon performance of the resulting policy was achieved by the adaptive MPC. In [56], accommodation and recovery from fault problems occurring in an octacopter were achieved using a combination of parameter estimation, RL, and model-based control. Fault-related parameters are estimated using an Unscented Kalman Filter (UKF) or a Particle Filter (PF).…”

Section: Policy-search-based Algorithmsmentioning

confidence: 99%

“…Hu and Wang [53] utilized an advanced PPO algorithm to optimize stochastic control strategies for quadrotor speed, outperforming traditional PID controllers. Kahn et al [34] and Bhan et al [56] addressed failure avoidance and fault compensation in UAV flights using RL and model-based control techniques. Other techniques integrate RL with deterministic controllers, enhancing trajectory tracking and stability during flight maneuvers.…”

Section: Policy-search-based Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Sönmez,

Rutherford,

Valavanis

2024

Drones

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Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time.

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Section: Policy-search-based Algorithmsmentioning

confidence: 99%

Section: Policy-search-based Algorithmsmentioning

confidence: 99%