Variance-Constrained Multi-Objective Stochastic Control and Filtering
Lifeng Ma - Nanjing University of Science and Technology, China
Zidong Wang - Brunel University, UK
Yuming Bo - Nanjing University of Science and Technology, China

In engineering practice, it is always desirable to design systems capable of meeting performance requirements such as stability, robustness, and reliability. However nonlinear and stochastic systems are more complex by nature, and with the ever-increasing degree of system complexity, multi-objective design with variance constraints has become a key research area.

Variance-Constrained Multi-Objective Stochastic Control and Filtering covers the latest research results and presents the state-of-the-art of control and filtering problems while taking multiple design objectives into consideration. It examines recent advances in control and filtering in the presence of incomplete information and discusses application potential in engineering practice. The reader will also benefit from the introduction of new concepts, new models and new methodologies with practical significance in control engineering and signal processing.

Key features:

• Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance
• Includes case studies of several nonlinear stochastic systems

• Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations
• Considers both time-invariant systems and time-varying systems
• Exploits newly developed techniques to handle the emerging mathematical and computational challenges