A Review on Distributed Control of Cooperating Mini UAVS

Ali, Qasim; Gageik, Nils; Montenegro, Sérgio

doi:10.5121/ijaia.2014.5401

Cited by 16 publications

(7 citation statements)

References 33 publications

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“…Table 4 shows the average time cost for solving a local problem (t lp ) and the average number of local optimizations (N lo ) to meet the terminal criterion in Eq. (22). The results show that SQP costs less time and involves a smaller optimization number than PSO, while the time cost of SQP can meet the requirement of real practice.…”

Section: Decentralized Optimization Based On Receding Horizon Optimizmentioning

confidence: 90%

“…21, the numerical computation algorithms should be adopted. The commonly used algorithms include sequential quadratic programming (SQP) [22], PSO [23] and mixed integer linear programming (MILP), which is solved by CPLEX [21].…”

Section: Decentralized Optimization Based On Receding Horizon Optimizmentioning

confidence: 99%

“…(21), the numerical computation algorithms should be adopted. The commonly used algorithms include sequential quadratic programming (SQP) [22], PSO [23] and mixed integer linear programming (MILP), which is solved by CPLEX [21]. Since the 4D trajectory generated by the improved tau-G strategy is continuous, SQP and PSO are carried out in order to solve decentralized 4D trajectory generation cases for the purpose of comparison.…”

Section: Decentralized 4d Trajectory Generation Based On Improved Taumentioning

confidence: 99%

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Decentralized 4D Trajectory Generation for UAVs Based on Improved Intrinsic Tau Guidance Strategy

Yang

Fang

2016

International Journal of Advanced Robotic Systems

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A decentralized four-dimensional (4D) trajectory generation method for unmanned aerial vehicles (UAVs), which uses the improved intrinsic tau gravity (tau-G) guidance strategy, is presented in this paper. Based on general tau theory, the current tau-G strategy can only generate the 4D trajectory with zero initial and final velocities, which is not appropriate for decentralized applications. By adding an initial velocity to the intrinsic movement of tau-G strategy, the improved tau-G strategy can synchronously guide the position and velocity to the desired values at the arrival time. In our decentralized 4D trajectory generation method, the improved tau-G strategy is used to plan the 4D trajectories for UAVs. To deal with environmental uncertainty and communication limitations, the receding horizon optimization driven by both sampling time and conflict events is utilized to renew trajectory parameters continually. The simulation results of challenging time-constrained tasks demonstrate that the proposed method can efficiently provide safer and lower-cost 4D trajectories.

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Section: Decentralized Optimization Based On Receding Horizon Optimizmentioning

confidence: 90%

Section: Decentralized Optimization Based On Receding Horizon Optimizmentioning

confidence: 99%

Section: Decentralized 4d Trajectory Generation Based On Improved Taumentioning

confidence: 99%

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Decentralized 4D Trajectory Generation for UAVs Based on Improved Intrinsic Tau Guidance Strategy

Yang

Fang

2016

International Journal of Advanced Robotic Systems

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“…The central objective in the theory of networked control systems is to address and analyze the practical challenges in implementations of real-world dynamical networks, in order to develop design algorithms with certified convergence properties [9,19,22,28,38,52,54,55]. The application areas, nowadays, range from multirobot systems [3] to social networks [23], power systems [20], metabolic pathways [11,44,46], and brain networks [8]. One of the inherent unappealing features of these real-world networks is the nonlinearity of the interactions among the subsystems that stem from how subsystems affect each other's dynamics [2,4,13,15,24,38].…”

Section: Introductionmentioning

confidence: 99%

Koopman Performance Analysis of Nonlinear Consensus Networks

Mousavi

Somarakis

Sun³

et al. 2020

Lecture Notes in Control and Information Sciences

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Spectral decomposition of dynamical systems is a popular methodology to investigate the fundamental qualitative and quantitative properties of these systems and their solutions. In this chapter, we consider a class of nonlinear cooperative protocols, which consist of multiple agents that are coupled together via an undirected state-dependent graph. We develop a representation of the system solution by decomposing the nonlinear system utilizing ideas from the Koopman operator theory and its spectral analysis. We use recent results on the extensions of the wellknown Hartman theorem for hyperbolic systems to establish a connection between the original nonlinear dynamics and the linearized dynamics in terms of Koopman spectral properties. The expected value of the output energy of the nonlinear protocol, which is related to the notions of coherence and robustness in dynamical networks, is evaluated and characterized in terms of Koopman eigenvalues, eigenfunctions, and modes. Spectral representation of the performance measure enables us to develop algorithmic methods to assess the performance of this class of nonlinear dynamical networks as a function of their graph topology. Finally, we propose a scalable computational method for approximation of the components of the Koopman mode decomposition, which is necessary to evaluate the systemic performance measure of the nonlinear dynamic network.

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“…These applications range from simple toys found in supermarkets to highly sophisticated applications like offshore wind power station monitoring and so forth [1]. Remarkable advantages over manned platforms include their much lower manufacturing and operational costs, risk avoidance for human pilots, flying safely low and slow, and realization of operations that are beyond inherent human limitations.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Control for Scalable Quadcopter Formations

Ali

Montenegro

2016

International Journal of Aerospace Engineering

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An innovative framework has been developed for teamwork of two quadcopter formations, each having its specified formation geometry, assigned task, and matching control scheme. Position control for quadcopters in one of the formations has been implemented through a Linear Quadratic Regulator Proportional Integral (LQR PI) control scheme based on explicit model following scheme. Quadcopters in the other formation are controlled through LQR PI servomechanism control scheme. These two control schemes are compared in terms of their performance and control effort. Both formations are commanded by respective ground stations through virtual leaders. Quadcopters in formations are able to track desired trajectories as well as hovering at desired points for selected time duration. In case of communication loss between ground station and any of the quadcopters, the neighboring quadcopter provides the command data, received from the ground station, to the affected unit. Proposed control schemes have been validated through extensive simulations using MATLAB5/Simulink5 that provided favorable results.

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A Review on Distributed Control of Cooperating Mini UAVS

Abstract: Mini Unmanned Aerial Vehicles (MUAVs)

Cited by 16 publications

References 33 publications

Decentralized 4D Trajectory Generation for UAVs Based on Improved Intrinsic Tau Guidance Strategy

Decentralized 4D Trajectory Generation for UAVs Based on Improved Intrinsic Tau Guidance Strategy

Koopman Performance Analysis of Nonlinear Consensus Networks

Decentralized Control for Scalable Quadcopter Formations

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