Distributed Formation Control Using Fuzzy Self-Tuning of Strictly Negative Imaginary Consensus Controllers in Aerial Robotics

• Published in: IEEE/ASME transactions on mechatronics Vol. 26; no. 5; pp. 2306 - 2315
• Main Authors: Tran, Vu Phi, Santoso, Fendy, Garratt, Matthew A., Petersen, Ian R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive strictly negative-imaginary (ASNI) controller; Closed loops; Control methods; Control systems; Control theory; Controllers; distributed unmanned aerial vehicle (UAV) formation control; Flight tests; Fuzzy control; fuzzy inference systems (FIS); Gusts; Knowledge based systems; Position measurement; Proportional integral derivative; Robotics; Robots; robust control; Robustness; Self tuning; Stability analysis; System theory; Systems theory; Transfer functions; Unmanned aerial vehicles; Vehicle dynamics
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2020.3036829

Wind gusts are significant barriers to the outdoor operations of networked multiple unmanned aerial vehicles (UAVs), which fly in close proximity to each other or around obstacles. As such, traditional control methods such as PID control may not perform adequately. Based on the strictly negative imaginary (SNI) systems theory, this article presents a novel decentralized and adaptive consensus-based formation control law that drives multiple UAVs to follow the desired formation in the presence of limited bandwidth for information exchange and dynamically changing environmental conditions. To be consistent with a decentralized approach, each UAV only measures its relative position with respect to its neighbors according to a fixed information graph. As a result, the required formation is obtained by maintaining the desired relative positions among UAVs. Moreover, to deal with the challenging dynamics of flight environments, we also employ a knowledge-based fuzzy inference system to automatically adjust the parameters of the SNI consensus controllers, leading to the development of a fast and robust adaption method. In this article, we conduct a stability analysis based on the SNI theorem and rigorously compare the performance of our controllers with respect to the performance of conventional PID controllers. The efficacy of the overall closed loop control system is highlighted in real-time flight tests.