Genetic Fuzzy Systems for Decentralized, Multi-UAV Cargo Handling

Bisig, Caleb; Montejo, Jorge B.; Verbryke, Matthew R.; Sathyan, Anoop; Ma, Ou

doi:10.2514/6.2020-1117

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…3) Cable Model: Cable-to-robot and cable-to-load connections are modeled as passive and mass-negligible rotational joints. Besides, the i-th cable is represented as a unilateral spring along its principal direction, which is a frequently adopted model [43], [33], [44], [45]. As commonly done in the state of the art, the cables' mass and inertia are assumed negligible in comparison to the robots' and load's.…”

Section: Introductionmentioning

confidence: 99%

Equilibria, Stability, and Sensitivity for the Aerial Suspended Beam Robotic System subject to Model Uncertainty

Gabellieri¹,

Tognon²,

Sanalitro³

et al. 2023

Preprint

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This work studies how parametric uncertainties affect the cooperative manipulation of a cable-suspended beamshaped load by means of two aerial robots not explicitly communicating with each other. In particular, the work sheds light on the impact of the uncertain knowledge of the model parameters available to an established communication-less force-based controller. First, we find the closed-loop equilibrium configurations in the presence of the aforementioned uncertainties, and then we study their stability. Hence, we show the fundamental role played in the robustness of the load attitude control by the internal force induced in the manipulated object by non-vertical cables. Furthermore, we formally study the sensitivity of the attitude error to such parametric variations, and we provide a method to act on the load position error in the presence of the uncertainties. Eventually, we validate the results through an extensive set of numerical tests in a realistic simulation environment including underactuated aerial vehicles and sagging-prone cables, and through hardware experiments.

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Section: Introductionmentioning

confidence: 99%

Equilibria, Stability, and Sensitivity for the Aerial Suspended Beam Robotic System subject to Model Uncertainty

Gabellieri¹,

Tognon²,

Sanalitro³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Nonlinear control requires the design of the algorithm to be robust enough and the modeling of the UAV system to be accurate enough. The third category is intelligent control technology, such as machine learning technology [27] and fuzzy control algorithm [28], which can achieve better results for complex systems with strong uncertainties. For the above control methods, the control force of the quadrotor increases with the increase of transportation distance, which generates the corresponding acceleration and speed.…”

Section: Introductionmentioning

confidence: 99%

A segmented energy‐based nonlinear tracking control method for quadrotor transport system

Ping

Wang

et al. 2021

Asian Journal of Control

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The safety of the quadrotor transport system (QTS) is always threatened by the suspension load swing under the fuselage during flight. At the same time, the QTS is affected by uncertainties such as changes in desired position and cable length. Trajectory planning is often neglected during the development of the controller. Moreover, there are some shortcomings in the most control methods such as the maximum speed, acceleration, payload swing, and transportation efficiency of the system that cannot be theoretically guaranteed. Therefore, a nonlinear tracking control method is proposed using the segmented energy, which can make the quadrotor move to the target position quickly and smoothly, meanwhile, suppress the heavy swing of the payload. Furthermore, it is proved that the method proposed in this paper can achieve the asymptotic stability of the system equilibrium point and converge to the expected value by Lyapunov theorem and LaSalle's invariance principle. Simulation results show that the control method achieves good control performance and strong robustness against system parameter uncertainty.

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Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems

Sathyan

Cohen

2021

Transportmetrica B: Transport Dynamics

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Genetic Fuzzy Systems for Decentralized, Multi-UAV Cargo Handling

Cited by 7 publications

References 11 publications

Equilibria, Stability, and Sensitivity for the Aerial Suspended Beam Robotic System subject to Model Uncertainty

Equilibria, Stability, and Sensitivity for the Aerial Suspended Beam Robotic System subject to Model Uncertainty

A segmented energy‐based nonlinear tracking control method for quadrotor transport system

Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems

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