In this paper, the robust fault-tolerant (FT) H ∞ control problem of active suspension systems with finite-frequency constraint is investigated. A full-car model is employed in the controller design such that the heave, pitch and roll motions can be simultaneously controlled. Both the actuator faults and external disturbances are considered in the controller synthesis. As the human body is more sensitive to the vertical vibration in 4–8Hz, robust H ∞ control with this finite-frequency constraint is designed. Other performances such as suspension deflection and actuator saturation are also considered. As some of the states such as the sprung mass pitch and roll angles are hard to measure, a robust H ∞ dynamic output-feedback controller with fault tolerant ability is proposed. Simulation results show the performance of the proposed controller.
An outline of health management for OWFs has been detailed in this chapter with description of various important elements. The need for such farm level management is explained and benefits are discussed. Key gaps to be filled in order to realize such a system are identified. The proposed health management system is mainly based on the existing knowledge of fleet-level management in the aerospace sector. Health management is much broader than CM; there are a number of aspects beyond the prognostics capabilities that are to be designed in order to arrive at a comprehensive maintenance management scheme. A comprehensive maintenance program that is sensitive to the health of the assets and adapts maintenance schedule accordingly, depending upon resource availability, logistics and inventory, is key to cost optimization while ensuring reliability and availability. The advances in CM and diagnostics in wind energy are in the right direction, and many of them are building blocks for health management. Offshore wind faces a number of unique challenges that can be satisfactorily addressed by following a suitable systematic approach. RCM implementation appears to be the most suitable as it encompasses other maintenance strategies and is suitable for farm-level deployment.
The emerging big data allows educational studies to examine teaching and learning behaviors over time and at scale. Less available is population-representative big data. This paper builds the first nationally representative sample of teachers’ online curation on a social media platform (i.e. Pinterest), the Public Instructional Network of School Resources (PINSR). This effort includes developing a big-rich data sampling framework, integrating social media data with administrative and census “ground truth” sources, and validating the population representativeness. Finally, we employ PINSR and present a worked example of teachers’ social media curation behavioral patterns across regions and time.
It is evident that deep text classification models trained on human data could be biased. In particular, they produce biased outcomes for texts that explicitly include identity terms of certain demographic groups. We refer to this type of bias as explicit bias, which has been extensively studied. However, deep text classification models can also produce biased outcomes for texts written by authors of certain demographic groups. We refer to such bias as implicit bias of which we still have a rather limited understanding. In this paper, we first demonstrate that implicit bias exists in different text classification tasks for different demographic groups. Then, we build a learning-based interpretation method to deepen our knowledge of implicit bias. Specifically, we verify that classifiers learn to make predictions based on language features that are related to the demographic attributes of the authors. Next, we propose a framework Debiased-TC to train deep text classifiers to make predictions on the right features and consequently mitigate implicit bias. We conduct extensive experiments on three real-world datasets. The results show that the text classification models trained under our proposed framework outperform traditional models significantly in terms of fairness, and also slightly in terms of classification performance.
Due to the inaccuracy and significant disturbance of the complex and harsh environment in real industrial processes, the traditional sensor devices cannot meet the high-performance requirement of measuring key quality variables. However, in practical industrial thickener cone systems, the underflow concentration is hard to measure and has a high cost and significant time delay. Furthermore, the higher encoder representation often causes information loss from the process variables. This paper presents a novel efficient dual long short-time memory (LSTM) method for concentration prediction in the deep cone thickener system. To this end, dual feedforward and inverse bidirectional long-short time memory are proposed for feature learning and long temporal prediction. The proposed framework introduces an averaging moving filtering to pass through features, therefore the performance of dual LSTM is increased by a large margin. In addition, the feedforward and reverse bidirectional LSTM are employed to learn the robust information without loss. At last, experimental verification of the performance of an industrial deep cone thickener demonstrates the proposed dual LSTM method outperforms other state-of-the-art methods.
Aimed at the challenge of wind turbine condition monitoring with insufficient available data caused by the harsh conditions, this paper presents a lightweight adaptive condition monitoring approach based on the back-propagation algorithm and the multivariate state estimation technique. In order to maximize the utility of available data, the multivariate state estimation technique is used to extract the features from multiple historical data. Moreover, buffer space is adopted to update dynamically while avoiding the effect of outliers. Then, the operator is modified so that the model can be trained and updated by the back-propagation algorithm, thereby achieving lightweight. The model performs well in condition monitoring with small samples, even discontinuous samples, that are verified through experiments based on field wind turbine datasets. The experimental results and comparative analyses demonstrate the effectiveness, generality, and advantage over others.
Creating a mathematical model of a vehicle crash is a task which involves considerations and analysis of different areas which need to be addressed because of the mathematical complexity of a crash event representation. Therefore, to simplify the analysis and enhance the modeling process, in this paper a novel wavelet-based approach is introduced to reproduce acceleration pulse of a vehicle involved in a crash event. The acceleration of a colliding vehicle is measured in its center of gravity—this crash pulse contains detailed information about vehicle behavior throughout a collision. Three types of signal analysis are elaborated here: time domain analysis (i.e. description of kinematics of a vehicle in time domain), the frequency analysis (identification of the parameters of the crash pulse in frequency domain), and the time–frequency analysis, which comprises those techniques that study a signal in both the time and frequency domains simultaneously, using Morlet wavelet properties. Determination of time of occurrence of particular frequency components included in the measured acceleration pulse and further analysis of the obtained scalegram are based on the reproduction of each crash pulse component, according to the frequencies identified in the acceleration signal. Finally, by using the superposition principle, those major signal components are combined, yielding the reproduced crash pulse. The comparative analysis between the current method's outcome, the responses of models established previously by using different approach and the behavior of a real car is performed and reliability of the actual methods and tools is evaluated.
In this paper. the method of direct torque control in the presence of a sliding-mode speed controller is proposed for a small wind turbine being used in water heating applications. This concept and control system design can be expanded to grid connected or off-grid applications. Direct torque control of electrical machines has shown several advantages including very fast dynamics torque control over field-oriented control. Moreover. the torque and flux controllers in the direct torque control algorithms are based on hvsteretic controllers which are nonlinear. In the presence of a sliding-mode speed control. a nonlinear control system can be constructed which is matched for AC/DC conversion of the converter that gives fast responses with low overshoots. The main control objectives of the proposed small wind turbine can be maximum power point tracking and soft-stall power control. This small wind turbine consists of permanent magnet synchronous generator and external wind speed. and rotor speed measurements are not required for the system. However. a sensor is needed to detect the rated wind speed overpass events to activate proper speed references for the wind turbine. Based on the low-cost design requirement of small wind turbines. an available wind speed sensor can be modified. or a new sensor can be designed to get the required measurement. The simulation results will be provided to illustrate the excellent performance of the closed-loop control system in entire wind speed range (4-25 m/s).
Discretization algorithm for real value attributes is of very important uses in many areas such as intelligence and machine learning. The algorithms related to Chi2 algorithm (includes modified Chi2 algorithm and extended Chi2 algorithm) are famous discretization algorithm exploiting the technique of probability and statistics. In this paper the algorithms are analyzed, and their drawback is pointed. Based on the analysis a new modified algorithm based on interval similarity is proposed. The new algorithm defines an interval similarity function which is regarded as a new merging standard in the process of discretization. At the same time, two important parameters (condition parameter<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>α</mml:mi></mml:mrow></mml:math>and tiny move parameter<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:math>) in the process of discretization and discrepancy extent of a number of adjacent two intervals are given in the form of function. The related theory analysis and the experiment results show that the presented algorithm is effective.
In this article, the issue of sliding mode control for nonlinear stochastic Markovian jump systems with uncertain time-varying delay is investigated. Considering the system state measurements and the state-dependent disturbances are not available for feedback purposes, an observer-based adaptive control strategy is proposed. Based on the decomposition of the input matrices, the state-space representation of the system is turned into a regular form with the aid of T-S fuzzy models first. Then, a fuzzy observer system is constructed, which could be transformed into two lower order subsystems. By choosing a common linear switching surface, on which it also obtains linear sliding mode dynamics in a simple form. Further, an adaptive controller is synthesized relying on the bounded system delay information to ensure the estimated states driven on the predefined sliding surface and remain the sliding motion. Also, the stochastic stability analysis of the sliding mode dynamics is undertaken with two types of transition rates, and an interesting result reveals that the stability for the dynamics with type of uncertain transition rates may cover the completely known type. Finally, a single-link robot arm model is provided to verify the validity of the proposed method.
This article studies consensus of linear multi-agent systems (MASs) on undirected graphs. An adaptive event-triggering protocol is constructed for consensus control by using relative information between agents. Sufficient conditions are established for consensus of linear MASs without and with external disturbances, respectively. Zeno behavior is proved to be excluded. Moreover, a self-triggered realization based on sampled information is formulated for the protocol. Since the proposed protocol incorporates both adaptive control and event-triggered control, it can be implemented in a fully distributed way and only makes use of the sampled relative information between neighboring agents. Two numerical examples are finally provided for demonstrating the effectiveness and advantages of the theoretical results.
This paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM) neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results.
