Buch, Englisch, 242 Seiten, Format (B × H): 164 mm x 241 mm, Gewicht: 496 g
Buch, Englisch, 242 Seiten, Format (B × H): 164 mm x 241 mm, Gewicht: 496 g
ISBN: 978-1-138-38657-0
Verlag: Taylor & Francis Ltd
Key Features
- Unifies existing and emerging concepts concerning stochastic control/filtering and distributed control/filtering with an emphasis on a variety of network-induced complexities
- Includes concepts like randomly occurring sensor failures and consensus in probability (with respect to time-varying stochastic multi-agent systems)
- Exploits the recursive linear matrix inequality approach, completing the square method, Hamilton-Jacobi inequality approach, and parameter-dependent matrix inequality approach to handle the emerging mathematical/computational challenges
- Captures recent advances of theories, techniques, and applications of stochastic control as well as filtering from an engineering-oriented perspective
- Gives simulation examples in each chapter to reflect the engineering practice
Zielgruppe
Professional and Professional Practice & Development
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1 Introduction. 2 Robust H1 Sliding Mode Control for Nonlinear Stochastic Systems with Multiple Data Packet Losses. 3 Sliding Mode Control for a Class of Nonlinear Discrete-Time Networked Systems with Multiple Stochastic Communication Delays. 4 Sliding Mode Control for Nonlinear Networked Systems with Stochastic Communication Delays. 5 Reliable H1 Control for A Class of Nonlinear Time-Varying Stochastic Systems with Randomly Occurring Sensor Failures. 6 Event-Triggered Mean Square Consensus Control for Time-Varying Stochastic Multi-Agent System with Sensor Saturations. 7 Mean-Square H1 Consensus Control for A Class of Nonlinear Time-Varying Stochastic Multi-Agent Systems: The Finite-Horizon Case. 8 Consensus Control for Nonlinear Multi-Agent Systems Subject to Deception Attacks. 9 Distributed Event-Based Set-Membership Filtering for A Class of Nonlinear Systems with Sensor Saturations over Sensor Networks. 10 Variance-Constrained Distributed Filtering for Time varying Systems with Multiplicative Noises and Deception Attacks over Sensor Networks. 11 Conclusions and Future Topics. Bibliography. Index.