A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances

• URL: http://arxiv.org/abs/2305.12543v1
• Date: Sun, 21 May 2023 19:00:06 GMT
• Title: A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances
• Authors: Ibrahim Ahmed and Marcos Quinones-Grueiro and Gautam Biswas
• Abstract summary: We present an approach to supervisory reinforcement learning control for unmanned aerial vehicles (UAVs) We formulate a supervisory control architecture that interleaves with extant embedded control and demonstrates robustness to environmental disturbances in the form of adverse wind conditions.
• Score: 1.8799681615947088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we present an approach to supervisory reinforcement learning control for unmanned aerial vehicles (UAVs). UAVs are dynamic systems where control decisions in response to disturbances in the environment have to be made in the order of milliseconds. We formulate a supervisory control architecture that interleaves with extant embedded control and demonstrates robustness to environmental disturbances in the form of adverse wind conditions. We run case studies with a Tarot T-18 Octorotor to demonstrate the effectiveness of our approach and compare it against a classic cascade control architecture used in most vehicles. While the results show the performance difference is marginal for nominal operations, substantial performance improvement is obtained with the supervisory RL approach under unseen wind conditions.

Related papers

• Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation [3.1498833540989413]
  This study presents a transformer-based approach for fault-tolerant control in fixed-wing Unmanned Aerial Vehicles (UAVs) Our method directly maps outer-loop reference values into control commands using the in-student learning and attention mechanisms of transformers. Experimental results demonstrate that our transformer-based controller outperforms industry-standard FCS and state-of-the-art reinforcement learning (RL) methods.
  arXiv  Detail & Related papers  (2024-11-05T10:24:45Z)
• Model-Free versus Model-Based Reinforcement Learning for Fixed-Wing UAV Attitude Control Under Varying Wind Conditions [1.474723404975345]
  This paper evaluates and compares the performance of model-free and model-based reinforcement learning for the attitude control of fixed-wing unmanned aerial vehicles using PID as a reference point. Results show that the Temporal Difference Model Predictive Control agent outperforms both the PID controller and other model-free reinforcement learning methods in terms of tracking accuracy and robustness.
  arXiv  Detail & Related papers  (2024-09-26T14:47:14Z)
• Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution [51.83951489847344]
  In robotics applications, smooth control signals are commonly preferred to reduce system wear and energy efficiency. In this work, we aim to bridge this performance gap by growing discrete action spaces from coarse to fine control resolution. Our work indicates that an adaptive control resolution in combination with value decomposition yields simple critic-only algorithms that yield surprisingly strong performance on continuous control tasks.
  arXiv  Detail & Related papers  (2024-04-05T17:58:37Z)
• Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning [66.10854214036605]
  A central question in robotics is how to design a control system for an agile mobile robot. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour.
  arXiv  Detail & Related papers  (2023-10-17T02:40:27Z)
• Designing a Robust Low-Level Agnostic Controller for a Quadrotor with Actor-Critic Reinforcement Learning [0.38073142980732994]
  We introduce domain randomization during the training phase of a low-level waypoint guidance controller based on Soft Actor-Critic. We show that, by introducing a certain degree of uncertainty in quadrotor dynamics during training, we can obtain a controller that is capable to perform the proposed task using a larger variation of quadrotor parameters.
  arXiv  Detail & Related papers  (2022-10-06T14:58:19Z)
• Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation [78.17108227614928]
  We propose a benchmark environment for Safe Reinforcement Learning focusing on aquatic navigation. We consider a value-based and policy-gradient Deep Reinforcement Learning (DRL) We also propose a verification strategy that checks the behavior of the trained models over a set of desired properties.
  arXiv  Detail & Related papers  (2021-12-16T16:53:56Z)
• Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations [50.37808220291108]
  This paper addresses learning safe output feedback control laws from partial observations of expert demonstrations. We first propose robust output control barrier functions (ROCBFs) as a means to guarantee safety. We then formulate an optimization problem to learn ROCBFs from expert demonstrations that exhibit safe system behavior.
  arXiv  Detail & Related papers  (2021-11-18T23:21:00Z)
• Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments [0.37798600249187286]
  We show that DRL can successfully learn to perform attitude control of a fixed-wing UAV operating directly on the original nonlinear dynamics. We deploy the learned controller on the UAV in flight tests, demonstrating comparable performance to the state-of-the-art ArduPlane proportional-integral-derivative (PID) attitude controller.
  arXiv  Detail & Related papers  (2021-11-07T19:07:46Z)
• Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts [2.198760145670348]
  We develop a robust form of a signal temporal logic to quantitatively evaluate the vehicle's behavior and measure the performance of controllers. We discuss the robustness of the obtained controllers, both to partial loss of power for one rotor and to wind gusts and finish by drawing conclusions on practical controller design by reinforcement learning.
  arXiv  Detail & Related papers  (2021-07-27T16:58:19Z)
• Regret-optimal Estimation and Control [52.28457815067461]
  We show that the regret-optimal estimator and regret-optimal controller can be derived in state-space form. We propose regret-optimal analogs of Model-Predictive Control (MPC) and the Extended KalmanFilter (EKF) for systems with nonlinear dynamics.
  arXiv  Detail & Related papers  (2021-06-22T23:14:21Z)
• Enforcing robust control guarantees within neural network policies [76.00287474159973]
  We propose a generic nonlinear control policy class, parameterized by neural networks, that enforces the same provable robustness criteria as robust control. We demonstrate the power of this approach on several domains, improving in average-case performance over existing robust control methods and in worst-case stability over (non-robust) deep RL methods.
  arXiv  Detail & Related papers  (2020-11-16T17:14:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.