Cooperative Control of Multi-Agent Systems
Optimal and Adaptive Design Approaches
Nonfiction, Science & Nature, Technology, Automation, Computers, Advanced Computing, Artificial Intelligence
| Author: | Frank L. Lewis, Hongwei Zhang, Kristian Hengster-Movric, Abhijit Das | ISBN: | 9781447155744 |
| Publisher: | Springer London | Publication: | December 31, 2013 |
| Imprint: | Springer | Language: | English |
| Author: | Frank L. Lewis, Hongwei Zhang, Kristian Hengster-Movric, Abhijit Das |
| ISBN: | 9781447155744 |
| Publisher: | Springer London |
| Publication: | December 31, 2013 |
| Imprint: | Springer |
| Language: | English |
Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs. It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design. Both continuous-time and discrete-time dynamical multi-agent systems are treated. Optimal cooperative control is introduced and neural adaptive design techniques for multi-agent nonlinear systems with unknown dynamics, which are rarely treated in literature are developed. Results spanning systems with first-, second- and on up to general high-order nonlinear dynamics are presented.
Each control methodology proposed is developed by rigorous proofs. All algorithms are justified by simulation examples. The text is self-contained and will serve as an excellent comprehensive source of information for researchers and graduate students working with multi-agent systems.
Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs. It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design. Both continuous-time and discrete-time dynamical multi-agent systems are treated. Optimal cooperative control is introduced and neural adaptive design techniques for multi-agent nonlinear systems with unknown dynamics, which are rarely treated in literature are developed. Results spanning systems with first-, second- and on up to general high-order nonlinear dynamics are presented.
Each control methodology proposed is developed by rigorous proofs. All algorithms are justified by simulation examples. The text is self-contained and will serve as an excellent comprehensive source of information for researchers and graduate students working with multi-agent systems.
