Analysis of Time Delays in Quadrotor Systems and Design of Control

Armah, Stephen K.; Yi, Sun

doi:10.1007/978-3-319-53426-8_20

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…In [48], the authors consider obstacle avoidance for tele-operated UAVs with time delays and evaluates the effectiveness of using wave variables in haptic feedback to improve operators' avoidance performance. The authors in [49] demonstrate how to use analytical solutions for ODEs and DDEs to estimate the time delay in Internet-based feedback control and quadrotor types of UAS. A feedback augmentation method is illustrated in [50] where the estimate of the change in the vehicle state due to the commands that are yet to affect the feedback is computed.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Tabassum,

Bai

2021

Preprint

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This paper develops and evaluates the performance of an allocation agent to be potentially integrated into the onboard Detect and Avoid (DAA) computer of an Unmanned Aircraft System (UAS). We consider a UAS that can be fully controlled by the onboard DAA system and by a remote human pilot. With a communication channel prone to latency, we consider a mixed initiative interaction environment, where the control authority of the UAS is dynamically allocated by the allocation agent. In an encounter with a dynamic intruder, the probability of collision may increase in the absence of pilot commands in the presence of latency. Moreover, a delayed pilot command may not result in safe resolution of the current scenario and need to be improvised. We design an optimization algorithm to reduce collision risk and refine delayed pilot commands. Towards this end, a Markov Decision Process (MDP) and its solution are employed to create a wait time map. The map consists of estimated times that the UAS can wait for the remote pilot commands at each state. A command blending algorithm is designed to select an avoidance maneuver that prioritizes the pilot intention extracted from the pilot commands. The wait time map and the command blending algorithm are implemented and integrated into a closed-loop simulator. We conduct ten thousands fast-time Monte Carlo simulations and compare the performance of the integrated setup with a standalone DAA setup. The simulation results show that the allocation agent enables the UAS to wait without inducing any near mid air collision (NMAC) and severe loss of well clear (LoWC) while positively improve pilot involvement in the encounter resolution.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Tabassum,

Bai

2021

Preprint

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“…In this paper, the constant delay applied in the drone's altitude control system was estimated using an approach based on analytical solutions to DDEs [19,20]. The time delay has been estimated as 0.36s in [21,22]. The altitude dynamics was assumed to be linear time-invariant (LTI) first order and the time delay, assumed to be single constant, was incorporated into the model as an explicit parameter.…”

Section: Robotics and Automation Engineering Journalmentioning

confidence: 99%

“…, a first order plant, used in [19,20] for the estimation of the delay, then this time-delay system will be neutral type, see [21,22] for analysis of such control system. Also, if 1 ( ) G s s = and then we have a P-control, retarded type time-delay, system.…”

Section: Non-adaptive Control: Pvmentioning

confidence: 99%

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Adaptive Control for Quadrotor UAVs Considering Time Delay: Study with Flight Payload

Armah

2018

RAEJ

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Design of effective control for unmanned aerial vehicles (UAVs) requires consideration of several sources of uncertainty. These undesired uncertainties affect the flight stability and performance in an unpredictable manner. The existence of transmission delays caused by wireless communication and payload variation are among such critical challenges. Adaptive control (AC) can lead to high performance tracking in the presence of uncertainties. This paper presents the application of model reference adaptive control (MRAC) to quadrotor types of UAVs considering the time delay in the altitude control system. MATLAB system identification tool is applied to obtain the altitude motion model, without time delay, for the quadrotor. Proportional-plus-velocity (PV) and PV-MRAC altitude control systems are designed, by incorporating an estimated constant time delay. The designed controllers are validated using simulations and flight tested in an indoor environment. The robustness of the PV-MRAC controller is tested against the baseline non-adaptive PV controller using the quadrotor's payload capability..

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“…(1) Reality gap. Flocking models that are stable in simulation under idealistic conditions tend to oscillate and destabilize quickly under real-life conditions when delays, uncertainties, and kinematic constraints are present (17)(18)(19)(20)(21).…”

Section: Introductionmentioning

confidence: 99%

Optimized flocking of autonomous drones in confined environments

et al. 2018

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We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.

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Analysis of Time Delays in Quadrotor Systems and Design of Control

Cited by 7 publications

References 25 publications

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Adaptive Control for Quadrotor UAVs Considering Time Delay: Study with Flight Payload

Optimized flocking of autonomous drones in confined environments

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