Lifeng Ma, Zidong Wang, Yuming Bo

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.

In this book, control and filtering problems for several classes of stochastic networked systems are discussed. In each chapter, the stability, robustness, reliability, consensus performance, and/or disturbance attenuation levels are investigated within a unified theoretical framework. The aim is to derive the sufficient conditions such that the resulting systems achieve the prescribed design requirements despite all the network-induced phenomena. Further, novel notions such as randomly occurring sensor failures and consensus in probability are discussed. Finally, the theories/techniques developed are applied to emerging research areas.
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