Simultaneous actuator and sensor fault estimation for neutral-type systems via intermediate observer

This study addresses the fault estimation (FE) issue for neutral-type systems with sensor faults and actuator faults through the intermediate observer. First, it is well-known that the observer matching condition (OMC) ought to be met for most traditional FE methods, which is actually difficult to satisfy for many systems. In order to overcome this limitation, a suitable variable is designed and the intermediate observer is proposed to estimate the actuator and sensor faults for neutral-type systems simultaneously. Second, based on linear matrix inequalities, sufficient conditions are derived, which guarantee the existence of the intermediate observer. An augmented descriptor system is constructed for the neutral-type systems. By the Lyapunov stability theory, states of error systems are ultimately bounded. Finally, two examples demonstrate the effectiveness and practicability of the designed strategy.

Implementation of Visual Sentiment Analysis on Flickr Images

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics and stickers. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state of the art works exploit the text associated with a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user, which usually includes text useful to maximize the diffusion of the social post. This System will extract three views: visual view, subjective text view and objective text view of Flickr images and will give sentiment polarity positive, negative or neutral based on the hypothesis table. Subjective text view gives sentiment polarity using VADER (Valence Aware Dictionary and sEntiment Reasoner) and objective text view gives sentiment polarity with three convolution neural network models. This system implements VGG-16, Inception-V3 and ResNet-50 convolution neural networks with pre pre-trained ImageNet dataset. The text extracted through these three convolution networks is given to VADER as input to find sentiment polarity. This system implements visual view using a bag of visual word model with BRISK (Binary Robust Invariant Scalable Key points) descriptor. System has a training dataset of 30000 positive, negative and neutral images. All the three views’ sentiment polarity is compared. The final sentiment polarity is calculated as positive if two or more views gives positive sentiment polarity, as negative if two or more views gives negative sentiment polarity and as neutral if two or more views gives neutral sentiment polarity. If all three views give unique polarity then the polarity of the objective text view is given as output sentiment polarity.

Fault-Tolerant Tracking Control for a Descriptor System under an Unknown Input Disturbances

The paper proposes a fault-tolerant tracking control scheme based on a robust observer for a descriptor system. Thus, it is assumed that the described system can be simultaneously occupied by an unknown input disturbance, along with an actuator and sensor faults. Additionally, it is natural to assume that the unknown input disturbance cannot be estimated, which makes the control process more difficult. Moreover, the proposed descriptor system is also occupied by external disturbances. Thus, the robust stability of the proposed control and estimation scheme was guaranteed by using H∞ performance. Consequently, the DC servo-motor laboratory system was used to confirm the correctness and effectiveness of the proposed fault-tolerant tracking control scheme.

Actuator Fault Diagnosis for Discrete-Time Systems via Augmenting State Approach

For the problem of the actuator fault diagnosis in the control systems, this paper presents a novel method by using an interval estimation approach to detect the faults and reconstruct them. In order to make estimations of the unavoidable measurement noise, a descriptor system form is built. Firstly, a full-order interval observer is developed to detect actuator faults for its sensitiveness to them. Then, a reduced-order one, which is robust to actuator faults, is presented. This method does not need the boundary information of faults; thus, the design condition is more relaxed. In order to make the interval observer stable and cooperative, linear matrix inequalities and a time-varying transformation are employed to ensure the error system matrix to be Schur and nonnegative. Based on the interval estimation results of the aforementioned method, an interval reconstruction method of actuator faults is proposed. Finally, results of the two simulation examples verify the proposed methods are effective and accurate.