Consensus-Based Decentralized Aerodynamic Moment Allocation Among Synthetic Jets and Control Surfaces

Mark, August; Xu, Yunjun; Dickinson, Benjamin T.

doi:10.1109/tcst.2018.2859908

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…which is an explicit solution of the optimal problem (12). Note that the existence of d in (14) and (16) makes the designed optimal desired states of actuators unimplementable, and hence, the construction of the estimates of these states should be considered first based upon observer (5), as follows:…”

Section: B Generator and Estimator Of Optimal Desired Statesmentioning

confidence: 99%

“…Various control allocation methods, e.g., pseudo-inverse, daisy chaining and direct allocation, have been developed and extensively investigated in literature [6]- [9], some of which have a particular focus on reconfigurable flight control design [10]- [12]. Apart from the basic control allocation functions, many advanced algorithms have considered additional factors, e.g., actuator energy saving and actuator safety, using techniques such as linear/nonlinear constrained quadratic programming [13], [14], additional dynamic augmentation [15] and input matrix factorization [16].…”

Section: Introductionmentioning

confidence: 99%

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On the Actuator Dynamics of Dynamic Control Allocation for a Small Fixed-Wing UAV With Direct Lift Control

Yan

Yang

Liu

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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A novel dynamic control allocation method is proposed for a small fixed-wing unmanned aerial vehicle (UAV), whose flaps can be actuated as fast as other control surfaces, offering an extra way of changing the lift directly. The actuator dynamics of this kind of UAVs, which may be sluggish comparing to the UAV dynamics, should also be considered in the control design. To this end, a hierarchical control allocation architecture is developed. A disturbance observer based high-level tracking controller is first designed to accommodate the lagging effect of the actuators and to compensate the adverse effect of external disturbances. Then, a dynamic control allocator based on a receding-horizon performance index is developed, which forces the actuator state in the low-level to follow the optimised reference. Compared to the conventional control allocation method that assumes ideal actuators with infinite bandwidths, higher tracking accuracy of the UAV and better energy efficiency can be achieved by the proposed method. Stability analysis and high fidelity simulations both demonstrate the effectiveness of the proposed method, which can be deployed on different fixed-wing UAVs with flaps to achieve better performance.

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Section: B Generator and Estimator Of Optimal Desired Statesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Actuator Dynamics of Dynamic Control Allocation for a Small Fixed-Wing UAV With Direct Lift Control

Yan

Yang

Liu

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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show abstract

“…The performance of consensus-based, multi-agent networks, such as the response to external stimuli, depends on rapidly transitioning from one operating point (consensus value) to another, e.g., in flocking of natural systems, [1], [2], as well as engineered systems such as autonomous vehicles, swarms of robots, e.g., [3]- [5] and other networked systems such as aerospace control [6] microgrids [7], [8], flexible structures [9]. Rapid cohesive transitions, e.g., in the orientation of the agents from one consensus value to another, is seen in flocking behaviour during predator attacks and migration [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Rapid Transitions with Robust Accelerated Delayed Self Reinforcement for Consensus-Based Networks

Tiwari,

Devasia

2020

Preprint

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Rapid transitions are important for quick response of consensus-based, multi-agent networks to external stimuli. While high-gain can increase response speed, potential instability tends to limit the maximum possible gain, and therefore, limits the maximum convergence rate to consensus during transitions. Since the update law for multi-agent networks with symmetric graphs can be considered as the gradient of its Laplacianpotential function, Nesterov-type accelerated gradient approaches from optimization theory, can further improve the convergence rate of such networks. An advantage of the accelerated-gradient approach is that it can be implemented using accelerated delayed-self-reinforcement (A-DSR), which does not require new information from the network nor modifications in the network connectivity. However, the accelerated-gradient approach is not directly applicable to non-symmetric graphs since the update law is not the gradient of the Laplacian-potential function. The main contributions of this work are to (i) extend the acceleratedgradient approach to general graph networks (whose Laplacians have real spectrum) using DSR, and (ii) develop analytical design criteria for a Robust A-DSR approach that maximizes both structural robustness and transition speed. Simulation results are presented to illustrate the performance improvement with the proposed Robust A-DSR of 40% in structural robustness and 50% in convergence rate to consensus, when compared to the case without the A-DSR. Moreover, experimental results are presented that show a similar 37% faster convergence with the Robust A-DSR when compared to the case without the A-DSR.

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Rapid Transitions With Robust Accelerated Delayed-Self-Reinforcement for Consensus-Based Networks

Tiwari

Devasia

2021

IEEE Trans. Contr. Syst. Technol.

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Consensus-Based Decentralized Aerodynamic Moment Allocation Among Synthetic Jets and Control Surfaces

Cited by 3 publications

References 31 publications

On the Actuator Dynamics of Dynamic Control Allocation for a Small Fixed-Wing UAV With Direct Lift Control

On the Actuator Dynamics of Dynamic Control Allocation for a Small Fixed-Wing UAV With Direct Lift Control

Rapid Transitions with Robust Accelerated Delayed Self Reinforcement for Consensus-Based Networks

Rapid Transitions With Robust Accelerated Delayed-Self-Reinforcement for Consensus-Based Networks

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