Time-varying UAV Formation Optimal Tracking Control with Dynamic Parameter Extended Control Barrier Functions

doi:10.12382/bgxb.2024.0260

Abstract

Abstract:

Aiming at the issue of optimal energy consumption of UAV time-varying formation under multiple constraints,an optimal control method based on an dynamic parameter extended high-order control barrier function is proposed.The method is used to improve the existing high-order control barrier function.In consideration of the motion states of UAV and obstacles,an the extended state dynamics model is established.The safety constraints of dynamic target obstacle avoidance are expanded on the extended state dynamics model,and the extended high-order control barrier function used to establish dynamic target obstacle avoidanceis obtained.For the feasibility and conservatism of the extended high-order control barrier function constraints,the adaptive rules of the class K function parameter are designed to obtain the dynamic parameter extended high-order control barrier function,which improves the feasibility and reduces the conservatism for the solution of constraint optimization problem.The control barrier function and the extended high-order control barrier function are used to establish the multi-constraints,and an objective function is established by the consistency condition and Hamiltonian analysis.The energy consumption optimization problem of time-varying formation is transformed into a multi-constraint optimization problem,and the quadratic programming is used to solve the problem to obtain the optimal control input.Simulated results demonstrate that the proposed method has superior obstacle avoidance and lower energy consumption compared to the fixed parameter high-order control obstacle functions and the artificial potential field.

Key words: time-varying formation tracking control, optimal control, control barrier function, state observer

CLC Number:

TJ301

WU Junqi, WU Bi, DENG Hongbin, ZHOU Zhiqian. Time-varying UAV Formation Optimal Tracking Control with Dynamic Parameter Extended Control Barrier Functions[J]. Acta Armamentarii, 2025, 46(4): 240260-.

Figures/Tables 12

Fig.1 Conservatism and feasibility of trajectory

Fig.2 Class K function adaptive rules

Fig.3 Control system framework

Fig.4 Formation communication topology

Fig.5 Observation results of velocity observer

Fig.6 UAV formation state at different times

Fig.7 Distance between UAV and obstacle at different times

Fig.8 Class K function parameter values

Fig.9 UAV obstacle avoidance trajectories with different class K function parameters

Fig.10 Control inputs with different class K function parameters

Table 1 The energy consumptions of different obstacle avoidance methods

方法	参数	UAV1	UAV2	UAV3	UAV4
E-HOCBF方法	p=0.1	4.33×10³	1.21×10⁴	1.40×10⁴	3.70×10³
	p=0.3	3.67×10³	1.07×10⁴	1.26×10⁴	3.21×10³
	p=0.5	3.68×10³	1.07×10⁴	1.24×10⁴	3.16×10³
DPE-HOCBF 方法	p=p_ik	3.29×10³	1.01×10⁴	1.21×10⁴	3.02×10³
APF方法		4.56×10³	1.04×10⁴	1.25×10⁴	3.70×10³

Table 2 Tracking error and mean simulation time

仿真条件	方法		平均仿真时间/s
无人机数量4、障碍物数量3	APF方法	0.7472	1.6062
无人机数量4、障碍物数量3	DPE-HOCBF方法	0.5064	1.6282
无人机数量8、障碍物数量6	APF方法	0.9407	1.5856
无人机数量8、障碍物数量6	DPE-HOCBF方法	0.6207	1.6078

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