다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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

With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

Although the Wireless Sensor and Actuator Networks (WSANs) have many advantages than the wired networks, the nature of sharing wireless media and the complicated behavior of Media Access Control (MAC) introduce adverse impacts on the control system. In contrast to most work on networked control systems using simplified models of network induced delays, this paper considers the study the random access delay caused by a contention-based MAC scheme slotted ALOHA in WSANs. An improved fault detection observer with access delay compensation is proposed to improve the fault detection performance against the MAC delays.

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Fault detection with MAC delay compensation in Wireless Sensor Actuator Networks

Generating mechanism of pathological beta oscillations in STN-GPe circuit model: A bifurcation study

In this paper, a novel resilient control technique is proposed for discrete-time stochastic Brownian systems with simultaneous unknown inputs and unexpected faults. Prior to previous work, the stochastic Brownian system under consideration is quite general, where stochastic perturbations exist in states, control inputs, uncertainties, and faults. Moreover, the unknown input uncertainties concerned cannot be fully decoupled. Innovative observer by employing augmented system approach, decomposition observer, and optimization algorithms is proposed to achieve simultaneous estimates of both states and faults. Furthermore, fault reconstruction-based signal compensation is formulated to alleviate the effects from actuator faults and sensor faults. An observer-based controller is eventually constructed to enhance the stability and robustness of the closed-loop dynamic system. The integrated resilient control technique can ensure the system has reliable output even under faults. Both linear systems and Lipschitz nonlinear systems are investigated and the design procedures are addressed, respectively. Finally, the proposed resilient control techniques are validated via an electromechanical servo-system, and an aircraft system.

Fault reconstruction and resilient control for discrete-time stochastic systems.

Nowadays, industrial plants are calling for high-performance fault diagnosis techniques to meet stringent requirements on system availability and safety in the event of component failures. This paper deals with robust fault estimation problems for stochastic nonlinear systems subject to faults and unknown inputs relying on Takagi-Sugeno fuzzy models. Augmented approach jointly with unknown input observers for stochastic Takagi-Sugeno models is exploited here, which allows one to estimate both considered faults and full system states robustly. The considered unknown inputs can be either completely decoupled or partially decoupled by observers. For the un-decoupled part of unknown inputs, which still influence error dynamics, stochastic input-to-state stability properties are applied to take nonzero inputs into account and sufficient conditions are achieved to guarantee bounded estimation errors under bounded unknown inputs. Linear matrix inequalities are employed to compute gain matrices of the observer, leading to stochastic input-to-state-stable error dynamics and optimization of the estimation performances against un-decoupled unknown inputs. Finally, simulation on wind turbine benchmark model is applied to validate the performances of the suggested fault reconstruction methodologies.

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Robust fault estimation for stochastic Takagi-Sugeno fuzzy systems

This article investigates the mean‐square strong stability and stabilization of a discrete‐time stochastic system corrupted by multiplicative noises. First, the definition of the mean‐square (MS) strong stability is addressed to avoid overshoots in system dynamics, and two necessary and sufficient conditions for the MS‐strong stability are derived. Moreover, the relationship between MS‐strong stability and MS‐stability is given. Second, some necessary and sufficient conditions of the MS‐strong stabilization via state feedback (SF) and output feedback are obtained, respectively. Furthermore, analytical expressions of SF controller and static output feedback (SOF) controller are proposed, respectively. Finally, an equivalent design method for SOF controller and dynamic output feedback controller is presented.

Zhiwei Gao

Papers

Fault detection with MAC delay compensation in Wireless Sensor Actuator Networks

Generating mechanism of pathological beta oscillations in STN-GPe circuit model: A bifurcation study

Fault reconstruction and resilient control for discrete-time stochastic systems.

Robust fault estimation for stochastic Takagi-Sugeno fuzzy systems

Mean‐square strong stability and stabilization of discrete‐time stochastic systems with multiplicative noises