Optimal Exact Control Allocation for Under-Actuated Multirotor Aerial Vehicles

Bezerra, José Agnelo; Santos, Davi Antônio dos

doi:10.1109/lcsys.2021.3110490

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…The actuator dynamics are modeled by first-order differential equations with a time constant of 0.01 s. The force and torque coefficients of the rotors are 1.2838 × 10 −5 N/(rad/s) 2 and 3.0811 × 10 −7 Nm/(rad/s) 2 , respectively. It is worth mentioning that the adopted model is typically used in the MAV literature to describe the set consisting of a brushless motor, an electronic speed controller, and a propeller [46].…”

Section: Scenario Simulation and Resultsmentioning

confidence: 99%

Cooperative Threat Engagement Using Drone Swarms

et al. 2023

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The ability of multiple manned and unmanned aircraft systems to cooperatively engage and disable an aerial threat plays a decisive role in modern warfare scenarios. In this paper, we apply key methods to enable the so-called cooperative threat engagement capability among multiple networked agents, e.g., a swarm of drones, with combat and communication capabilities. In particular, this research combines AI-based decision-making and control techniques for a swarm of loyal wingman drones to coordinate efficient defense actions in a cooperative and autonomous manner. We apply these concepts in a defense scenario that is modeled to analyze the loyal wingman concept, which we consider an interesting testbed for cooperative decision-making and low-level control techniques. The investigated methods were implemented in a realistic 3D UAV simulator for demonstration and evaluation. INDEX TERMSCooperative engagement capability, loyal wingman UAV, decision making, manned-unmanned teaming, sliding mode control.

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Section: Scenario Simulation and Resultsmentioning

confidence: 99%

Cooperative Threat Engagement Using Drone Swarms

et al. 2023

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“…, h}, and Ψ(|•|) diag(|•|) maps a vector into a diagonal matrix. The SMD (35) initial conditions are set as z d 0 (0) = σ(0) and z d i (0) = 0 3 , ∀i ∈ {1, . .…”

Section: Position Control Lawmentioning

confidence: 99%

“…The control allocation calculates f r from f and τc b as a solution to the control allocation Equation [35]:…”

Section: Control Allocationmentioning

confidence: 99%

Robust Collision-Free Guidance and Control for Underactuated Multirotor Aerial Vehicles

Ricardo Jr,

Santos

2023

Drones

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This paper is concerned with the robust collision-free guidance and control of underactuated multirotor aerial vehicles in the presence of moving obstacles capable of accelerating, linear velocity and rotor thrust constraints, and matched model uncertainties and disturbances. We address this problem by using a hierarchical flight control architecture composed of a supervisory outer-loop guidance module and an inner-loop stabilizing control one. The inner loop is designed using a typical hierarchical control scheme that nests the attitude control loop inside the position one. The effectiveness of this scheme relies on proper time-scale separation (TSS) between the closed-loop (faster) rotational and (slower) translational dynamics, which is not straightforward to enforce in practice. However, by combining an integral sliding mode attitude control law, which guarantees instantaneous tracking of the attitude commands, with a smooth and robust position control one, we enforce, by construction, the satisfaction of the TSS, thus avoiding the loss of robustness and use of a dull trial-and-error tweak of gains. On the other hand, the outer-loop guidance is built upon the continuous-control-obstacles method, which is incremented to respect the velocity and actuator constraints and avoid multiple moving obstacles that can accelerate. The overall method is evaluated using a numerical Monte Carlo simulation and is shown to be effective in providing satisfactory tracking performance, collision-free guidance, and the satisfaction of linear velocity and actuator constraints.

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