Preview-based leader-following consensus control of distributed multi-agent systems
| Autor: | C. L. Philip Chen, Chang-E Ren, Guilu Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Information Systems and Management
Computer science Multi-agent system 05 social sciences 050301 education 02 engineering and technology Linear-quadratic regulator Directed acyclic graph Computer Science Applications Theoretical Computer Science Computer Science::Multiagent Systems Consensus control Consensus Computer Science::Systems and Control Artificial Intelligence Control and Systems Engineering Control theory Step function 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing State (computer science) 0503 education Software |
| Zdroj: | Information Sciences. 559:251-269 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2020.12.081 |
| Popis: | The preview-based leader-following consensus control of distributed multi-agent systems (MAS) with a directed acyclic graph is investigated in this paper. The external disturbance considered in this paper is the step function that can be previewed, and the information of the leader can be obtained in advance. The leader-following consensus control of MAS is solved by using the state augmentation technique and the preview control method that transforms the consensus control problem into the finite horizon linear quadratic regulator (LQR) problem and the infinite horizon LQR problem. In the finite horizon, the minimum principle is utilized to design the optimal controller. In the infinite horizon, the standard infinite horizon LQR is applied to devise the corresponding controller. Assuming the original systems are stabilisable and detectable, the distributed consensus controller guarantees the asymptotic consensus of the preview MAS. The simulation illustrates the effectiveness of the distributed leader-following consensus controller designed in this paper. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect