Multi Model Jumping Systems Robust Filtering And Fault Detection
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Multi-model Jumping Systems: Robust Filtering and Fault Detection
This book focuses on multi-model systems, describing how to apply intelligent technologies to model complex multi-model systems by combining stochastic jumping system, neural network and fuzzy models. It focuses on robust filtering, including finite-time robust filtering, finite-frequency robust filtering and higher order moment robust filtering schemes, as well as fault detection problems for multi-model jump systems, such as observer-based robust fault detection, filtering-based robust fault detection and neural network-based robust fault detection methods. The book also demonstrates the validity and practicability of the theoretical results using simulation and practical examples, like circuit systems, robot systems and power systems. Further, it introduces readers to methods such as finite-time filtering, finite-frequency robust filtering, as well as higher order moment and neural network-based fault detection methods for multi-model jumping systems, allowing them to grasp the modeling, analysis and design of the multi-model systems presented and implement filtering and fault detection analysis for various systems, including circuit, network and mechanical systems.
Robust Filtering and Fault Detection for T-S Fuzzy Systems
This book conducts an in-depth research on robust filtering and fault detection for a class of T-S fuzzy systems. On the basis of the existing research on T-S fuzzy theory, robust filtering theory, and fault diagnosis theory, some new and effective technologies are proposed to solve the problems of robust filtering and fault detection for a class T-S fuzzy systems, while overcoming the shortcomings and limitations of the existing solutions. This book introduces new design solutions for a class of T-S fuzzy systems to address the existing problems in the research of robust filtering and fault detection, namely 1) two new filtering methods are explored to obtain better filtering results than the existing approaches; 2) a new event-triggered filtering scheme is proposed for T-S fuzzy systems with bounded disturbances, which realizes that the designed observer gains in the absence of event-triggered mechanisms are also applicable to the case with event-triggered mechanisms; 3) two new methods are constructed to deal with the asynchronous problems of premise variables effectively, which overcome the defects and limitations of the existing ones; and 4) an effective fault detection scheme for handling measurement outliers is constructed, which can avoid the occurrence of false alarms. This book is intended to inspire researchers and engineers, offering deeper insights into robust filtering and fault detection for T-S fuzzy systems, and equipping them with the latest advancements in fuzzy system theory, robust filtering, and fault diagnosis. It also provides valuable theoretical references for engineers tackling practical engineering problems.
Non-monotonic Approach to Robust H? Control of Multi-model Systems
Non-monotonic Approach to Robust H8 Control of Multi-model Systems focuses on robust analysis and synthesis problems for multi-model systems based on the non-monotonic Lyapunov Functionals (LFs) approach that enlarges the stability region and improves control performance. By fully considering the diversity of switching laws, the multi-step time difference, the multi-step prediction, and the expansion of system dimension, the non-monotonic LF can be properly constructed. The focus of this book is placed on the H8 state feedback control, H8 filtering and H8 output feedback control for multi-model systems via a non-monotonic LF approach. The book's authors provide illustrative examples to show the feasibility and efficiency of the proposed methods, along with practical examples that demonstrate the effectiveness and potential of theoretical results.