Distributed data-driven UAV formation control via evolutionary games: Experimental results

Barreiro-Gómez, Julian; Mas, Ignacio; Giribet, Juan I.; Moreno, Patricio; Ocampo‐Martínez, Carlos; Peña, Ricardo; Quijano, Nicanor

doi:10.1016/j.jfranklin.2021.05.002

Cited by 15 publications

(5 citation statements)

References 46 publications

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“…For example, an effective event-driven cooperative formation control protocol is proposed for multiple UAVs to reduce the communication frequency in [8,9]. Moreover, a novel data-driven distributed control approach based on evolutionary games is proposed to solve the multi-UAV formation tracking problem subject to communication constraints change over time in [10]. Although the aforementioned control schemes can realize the formation keeping and tracking of multiple UAVs, two-fold system performance indicators should be considered in the relevant formation controller design, i.e., the finitetime convergence performance and anti-saturated performance.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Anti-Saturated Formation Tracking Control of Multiple Unmanned Aerial Vehicles: A Performance Tuning Way

Chen,

Lei,

Peng

et al. 2023

Mathematics

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A highly effective control method is very important to guarantee the safety of the formation of flying missions for multiple unmanned aerial vehicles (UAVs), especially in the presence of complex flying environments and actuator constraints. In this regard, this paper investigates the formation tracking control problem of multiple UAVs in the presence of actuator saturation. Firstly, a brand-novel finite-time anti-saturated control scheme is proposed for multiple UAVs to track the desired position commands, wherein the tracking performance is tuned by introducing a logarithmic function-based state-mapping policy. Then, an adaptive scheme based on projection rules is devised to compensate for the negative effects brought by the actuator saturation. Based on the proposed formation tracking controller, the finite-time formation tracking performance tuning and control saturation problems can be addressed simultaneously with a comparatively allowable system robustness. Finally, three groups of illustrative examples are organized to verify the effectiveness of the proposed formation tracking control scheme.

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Section: Introductionmentioning

confidence: 99%

Finite-Time Anti-Saturated Formation Tracking Control of Multiple Unmanned Aerial Vehicles: A Performance Tuning Way

Chen,

Lei,

Peng

et al. 2023

Mathematics

View full text Add to dashboard Cite

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“…Numerous scholars have utilized evolutionary game theory to study cooperation problems, yielding significant results that have been widely applied to practical issues [24][25][26]. Given that the concept of stable cooperation and target assignment in a multi-UAV system, under limited information and resources, aligns with the principles of evolutionary game theory, a comprehensive analysis of the stable long-term trends of both parties engaged in the game becomes possible by leveraging these principles.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Game-Theoretic Approach to Unmanned Aerial Vehicle Network Target Assignment in Three-Dimensional Scenarios

Gao,

Zhang,

Wang

et al. 2023

Mathematics

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Target assignment has been a hot topic of research in the academic and industrial communities for swarms of multiple unmanned aerial vehicle (multi-UAVs). Traditional methods mainly focus on cooperative target assignment in planes, and they ignore three-dimensional scenarios for the multi-UAV network target assignment problem. This paper proposes a method for target assignment in three-dimensional scenarios based on evolutionary game theory to achieve cooperative targeting for multi-UAVs, significantly improving operational efficiency and achieving maximum utility. Firstly, we construct an evolutionary game model including game participants, a tactical strategy space, a payoff matrix, and a strategy selection probability space. Then, a multi-level information fusion algorithm is designed to evaluate the overall attack effectiveness of multi-UAVs against multiple targets. The replicator equation is leveraged to obtain the evolutionarily stable strategy (ESS) and dynamically update the optimal strategy. Finally, a typical scenario analysis and an effectiveness experiment are carried out on the RflySim platform to analyze the calculation process and verify the effectiveness of the proposed method. The results show that the proposed method can effectively provide a target assignment solution for multi-UAVs.

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“…Trajectory tracking control of the vehicle aims to combine the actual deviation between the actual vehicle location and ideal trajectory, and minimize the lateral deviation and heading deviation through steering control, to achieve dynamic tracking of the referenced trajectory [9,10]. In order to guarantee the driving safety and comfort in trajectory tracking control, while taking into account the high precision and stability requirements of trajectory tracking control, Kim E et al [11] applied the model predictive control method to propose a trajectory tracking method, to achieve accurate and smooth tracking.…”

Section: Introduction 1rationalementioning

confidence: 99%

Intelligent Vehicle Trajectory Tracking Control Based on VFF-RLS Road Friction Coefficient Estimation

et al. 2022

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This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control lies in the fast and accurate identification of road friction coefficients. Therefore, an improved strategy is designed based on traditional recursive least squares (RLS), which is utilized for accurate identification of the friction coefficient. First, the tire force and slip rate required for the estimation of the road friction coefficient by constructing the vehicle dynamics model and tire effective model are calculated. In this paper, a variable forgetting factor recursive least squares (VFF-RLS) method is proposed for the construction of the friction coefficient estimator. Second, the identified results are output to the model predictive controller (MPC) constructed in this paper as a way to improve tire slip angle constraints, to realize the trajectory tracking of the intelligent vehicle. Finally, the joint simulation test results of Carsim and Matlab/Simulink show that the trajectory tracking system based on the VFF-RLS friction coefficient estimator has outstanding tracking performance.

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Distributed data-driven UAV formation control via evolutionary games: Experimental results

Cited by 15 publications

References 46 publications

Finite-Time Anti-Saturated Formation Tracking Control of Multiple Unmanned Aerial Vehicles: A Performance Tuning Way

Finite-Time Anti-Saturated Formation Tracking Control of Multiple Unmanned Aerial Vehicles: A Performance Tuning Way

An Evolutionary Game-Theoretic Approach to Unmanned Aerial Vehicle Network Target Assignment in Three-Dimensional Scenarios

Intelligent Vehicle Trajectory Tracking Control Based on VFF-RLS Road Friction Coefficient Estimation

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