Improved Gradient-descent-based Control Algorithms for Multi-agent Systems With Fixed and Switching Topology

• Published in: International journal of control, automation, and systems Vol. 21; no. 11; pp. 3673 - 3683
• Main Authors: Lu, Jiahang, Li, Xiuying
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.11.2023; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Adaptive algorithms; Adaptive control; Control; Control theory; Engineering; Machine learning; Mechatronics; Multiagent systems; Optimization; Regular Papers; Robotics; Switching; Topology; 제어계측공학; Consensus tracking; multi-agent systems; gradient descent methods; model free adaptive control
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-022-0513-x

In this paper, model free adaptive control algorithms are proposed based on ten improved gradient descent methods which are commonly used as optimization algorithms in deep learning. For the designed control scheme, the modelling, control and optimization can be integrated in a unified framework. The effects of ten algorithms on the consensus tracking performance in multi-agent systems are studied and compared. In order to get a more universal conclusion, systems with fixed and switching topology are considered respectively. Simulation results show that the model free adaptive control algorithm based on adaptive momentum estimation method with decoupled weight decay (AdamW) has optimal performance.