A most critical and important issue surrounding the design of automatic control systems with the successively increasing complexity is guaranteeing a high system performance over a wide operating range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solutions, advanced fault detection and identification (FDI) technology is receiving considerable attention. The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.

Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools

A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation

This paper addresses the robust H∞ filtering problem for a class of discrete time-varying Markovian jump systems with randomly occurring nonlinearities and sensor saturation. Two kinds of transition probability matrices for the Markovian process are considered, namely, the one with polytopic uncertainties and the one with partially unknown entries. The nonlinear disturbances are assumed to occur randomly according to stochastic variables satisfying the Bernoulli distributions. The main purpose of this paper is to design a robust filter, over a given finite-horizon, such that the H∞ disturbance attenuation level is guaranteed for the time-varying Markovian jump systems in the presence of both the randomly occurring nonlinearities and the sensor saturation. Sufficient conditions are established for the existence of the desired filter satisfying the H∞ performance constraint in terms of a set of recursive linear matrix inequalities. Simulation results demonstrate the effectiveness of the developed filter design scheme.

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Robust ${{\cal H}}_{\infty}$ Filtering for Markovian Jump Systems With Randomly Occurring Nonlinearities and Sensor Saturation: The Finite-Horizon Case

This paper is concerned with the distributed finite-horizon filtering problem for a class of time-varying systems over lossy sensor networks. The time-varying system (target plant) is subject to randomly varying nonlinearities (RVNs) caused by environmental circumstances. The lossy sensor network suffers from quantization errors and successive packet dropouts that are described in a unified framework. Two mutually independent sets of Bernoulli distributed white sequences are introduced to govern the random occurrences of the RVNs and successive packet dropouts. Through available output measurements from not only the individual sensor but also its neighboring sensors according to the given topology, a sufficient condition is established for the desired distributed finite-horizon filter to ensure that the prescribed average filtering performance constraint is satisfied. The solution of the distributed filter gains is characterized by solving a set of recursive linear matrix inequalities. A simulation example is provided to show the effectiveness of the proposed filtering scheme.

Distributed Filtering for a Class of Time-Varying Systems Over Sensor Networks With Quantization Errors and Successive Packet Dropouts

Residual life estimation based on a generalized Wiener degradation process

The strong tracking filter (STF) proposed by Zhou et al. in 1992, which was developed for nonlinear systems with white noise, is extended to a class of nonlinear time-varying stochastic systems with coloured noise. A new concept of‘softening factor’is introduced to make the state estimator much smoother; its value can be preselected by computer simulations via a heuristic searching scheme. The STF is then used to estimate the parameters of a class of nonlinear time-varying stochastic systems in the presence of coloured noise. The robustness against model uncertainty of the STF is thoroughly studied via Monte Carlo simulations. The results show that the STF has strong robustness against model-plant parameter mismatches in the statistics of the initial conditions, the statistics of the process noise and the measurement noise, the system parameters, and the parameters in the measurement noise model. To a great extent the STF can give bias-free parameter estimations, where the parameters may be randomly time ...

Strong tracking filtering of nonlinear time-varying stochastic systems with coloured noise: application to parameter estimation and empirical robustness analysis

D. H. Zhou

Papers

Strong tracking filtering of nonlinear time-varying stochastic systems with coloured noise: application to parameter estimation and empirical robustness analysis