Affiliation:
1. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
2. Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Guangzhou 510640, China
3. Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems, Guangzhou 510640, China
Abstract
This paper considers the pursuit problem of a moving target by a swarm of drones through a flexible-configuration formation. The drones are modeled by second-order systems subject to uncertain damping ratios, whereas the moving target follows a polynomial-type trajectory whose coefficient vectors are fully unknown. Due to location denial, drones cannot obtain their absolute positions, but they can obtain their positions relative to other neighboring drones and the target. To achieve a robust formation pursuit, a robust cooperative control protocol is synthesized, which comprises three key components, namely, the pseudo drone position estimator, the pseudo target position estimator, and the local internal model control (IMC) law. The pseudo drone position estimator and the pseudo target position estimator aim to recover for each drone the position of itself and the target, respectively, but are subject to some common unknown constant bias in a distributed manner. By subtracting the pseudo target position from the pseudo drone position, each drone can acquire its position relative to the target, which facilitates the design of a local IMC law to fulfill formation pursuit in the presence of system parametric uncertainties. Both pure numerical simulation and hardware-in-the-loop (HIL) simulation are performed to verify the effectiveness of the proposed control protocol.
Funder
National Natural Science Foundation of China
Guangdong Natural Science Foundation
Fundamental Research Funds for the Central Universities
Reference77 articles.
1. Cooperative load transport: A formation-control perspective;Bai;IEEE Trans. Robot.,2010
2. Distributed geodesic control laws for flocking of nonholonomic agents;Moshtagh;IEEE Trans. Autom. Control,2007
3. Optimized multi-agent formation control based on an identifier–actor–critic reinforcement learning algorithm;Wen;IEEE Trans. Fuzzy Syst.,2017
4. Enclosing a target by nonholonomic mobile robots with bearing-only measurements;Zheng;Automatica,2015
5. Benda, M., Jagannathan, V., and Dodhiawala, R. (1986). On optimal cooperation of knowledge sources: An empirical investigation. Technical Report BCS-G2010-28, Boeing Advanced Technology Center, Boeing Computing Services.