Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

An adaptive control system uses a neural network to provide adaptive control when the plant is operating within a normal operating range, but shifts to other types of control as the plant operating conditions move outside of the normal operating range. The controller uses a structure which allows the neural network parameters to be determined from minimal information about plant structure and the neural network is trained on-line during normal plant operation. The resulting system can be proven to be stable over all possible conditions. Further, with the inventive techniques, the tracking accuracy can be controlled by appropriate network design.

Stable adaptive neural network controller

https://www.fossen.biz/home/papers/JohansenFossen%20Automatica%202013.pdf

Control allocation—A survey

This note investigates process fault accommodation in a class of nonlinear continuous-time systems. A new fault estimation module, based on an adaptive estimator, is first proposed. The fault tolerant controller is constructed to compensate for the effect of the faults by stabilizing the closed-loop system. A flexible joint robotic example is given to illustrate the efficiency of the proposed approach

Fault Accommodation for Nonlinear Dynamic Systems

Direct adaptive-state feedback control schemes are developed for linear time-invariant plants with actuator failures with characterizations that some of the plant inputs are stuck at some fixed or varying values which cannot be influenced by control action. Conditions and controller structures for achieving plant-model state matching in the presence of actuator failures are derived. Adaptive laws are designed for updating the controller parameters when both the plant parameters and actuator-failure parameters are unknown. Closed-loop stability and asymptotic-state tracking are ensured. Simulation results show that desired system performance is achieved with the developed adaptive actuator failure compensation control designs.

Adaptive state feedback and tracking control of systems with actuator failures

Reducing the impact of seasonal influenza epidemics and other pandemics such as the H1N1 is of paramount importance for public health authorities. Studies have shown that effective interventions can be taken to contain the epidemics if early detection can be made. Traditional approach employed by the Centers for Disease Control and Prevention (CDC) includes collecting influenza-like illness (ILI) activity data from “sentinel” medical practices. Typically there is a 1–2 week delay between the time a patient is diagnosed and the moment that data point becomes available in aggregate ILI reports. In this paper we present the Social Network Enabled Flu Trends (SNEFT) framework, which monitors messages posted on Twitter with a mention of flu indicators to track and predict the emergence and spread of an influenza epidemic in a population. Based on the data collected during 2009 and 2010, we find that the volume of flu related tweets is highly correlated with the number of ILI cases reported by CDC. We further devise auto-regression models to predict the ILI activity level in a population. The models predict data collected and published by CDC, as the percentage of visits to “sentinel” physicians attributable to ILI in successively weeks. We test models with previous CDC data, with and without measures of Twitter data, showing that Twitter data can substantially improve the models prediction accuracy. Therefore, Twitter data provides real-time assessment of ILI activity.

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Predicting Flu Trends using Twitter data

The paper describes the design of an automatic control reconfiguration scheme for accommodation of actuator failures in a class of plants where the number of control inputs is larger than the number of controlled outputs. One of the main features of the proposed scheme is that the control reconfiguration is achieved automatically based only on the response of the overall system. The method is developed for the case when one or more actuators freeze in a certain position and do not respond to subsequent commands. We assumed that the information about the failure is not available to the controller. To solve this problem, a control law with adjustable parameters is introduced, and, using a convenient parametrization of the overall system, the resulting error model is expressed in a somewhat modified form as compared to those arising in standard adaptive control. The design of the corresponding adaptive laws based on Lyapunov analysis allow us to prove global stability of the overall scheme in the presence of multiple actuator failures.

A stable scheme for automatic control reconfiguration in the presence of actuator failures

Currently, advanced control systems implemented on production ground vehicles have the goal of promoting maneuverability and stability. With proper coordination of steering and braking action, these goals may be achieved even when road conditions are severe. This paper considers the effect of steering and wheel torques on the dynamics of vehicular systems. Through the input-output linearization technique, the advantages of four-wheel steering (4WS) system and independent torques control are clear from a mathematical point of view. A sliding mode controller is also designed to modify driver’s steering and braking commands to enhance maneuverability and safety. Simulation results show the maneuverability and safety are improved. Although the controller design is based on a four-wheel steering vehicle, the algorithm can also be applied to vehicles of different configurations with slight changes.

A Global Approach to Vehicle Control: Coordination of Four Wheel Steering and Wheel Torques

Seasonal influenza epidemics causes severe illnesses and 250,000 to 500,000 deaths worldwide each year Other pandemics like the 1918 “Spanish Flu” may change into a devastating one Reducing the impact of these threats is of paramount importance for health authorities, and studies have shown that effective interventions can be taken to contain the epidemics, if early detection can be made In this paper, we introduce the Social Network Enabled Flu Trends (SNEFT), a continuous data collection framework which monitors flu related tweets and track the emergence and spread of an influenza We show that text mining significantly enhances the correlation between the Twitter and the Influenza like Illness (ILI) rates provided by Centers for Disease Control and Prevention (CDC) For accurate prediction, we implemented an auto-regression with exogenous input (ARX) model which uses current Twitter data, and CDC ILI rates from previous weeks to predict current influenza statistics Our results show that, while previous ILI data from CDC offer a true (but delayed) assessment of a flu epidemic, Twitter data provides a real-time assessment of the current epidemic condition and can be used to compensate for the lack of current ILI data We observe that the Twitter data is highly correlated with the ILI rates across different regions within USA and can be used to effectively improve the accuracy of our prediction Our age-based flu prediction analysis indicates that for most of the regions, Twitter data best fit the age groups of 5-24 and 25-49 years, correlating well with the fact that these are likely, the most active user age groups on Twitter Therefore, Twitter data can act as supplementary indicator to gauge influenza within a population and helps discovering flu trends ahead of CDC

/pdf/twitter-improves-seasonal-influenza-prediction-h076iz09g9.pdf

Twitter improves seasonal influenza prediction

In this paper we considered the existing stable adaptive fault-tolerant control system design from references [3, 4] for achieving acceptable flight performance in the presence of control effector failures. This controller is developed for the overactuated case, i.e. the case when the number of control effectors exceeds the number of controlled outputs. We have shown that, in some cases, single or multiple effector failures introduce disturbances that are too large to be effectively handled by a single adaptive controller. To address this problem, in this paper we developed an adaptive fault-tolerant flight control system based on the concept of multiple models, switching and tuning from reference [11]. We have shown that such a controller yields a stable overall system, is sufficiently robust in the presence of severe control effector failures, and achieves excellent overall performance as illustrated through simulations of F/A-18A carrier landing maneuver.

Ssu-Hsin Yu

Papers

Predicting Flu Trends using Twitter data

A stable scheme for automatic control reconfiguration in the presence of actuator failures

A Global Approach to Vehicle Control: Coordination of Four Wheel Steering and Wheel Torques

Twitter improves seasonal influenza prediction

Stable adaptive fault-tolerant control of overactuated aircraft using multiple models, switching and tuning *