In this paper, the decentralized optimal control problem is addressed for a class of large-scale systems subject to injection attacks. All subsystem matrices are considered to be unavailable to the designer. A model-free decentralized sliding mode control (SMC) scheme for each subsystem is designed via just utilizing its own state information and the known bounds of the interconnections and the injection attacks. Moreover, the adaptive dynamic programming (ADP) approach is incorporated to deal with the infinite horizon optimal control problem for the sliding mode dynamics, which is equivalent to the solution of a set of parallel algebraic Riccati equations. Furthermore, a novel parallel policy iteration algorithm is developed to implement the proposed decentralized SMC scheme without using all subsystems dynamics matrices. Specifically, it is shown that during the whole policy iteration steps, the reachability of each sliding variable and the stability of each sliding mode dynamics are guaranteed simultaneously by the online updating decentralized SMC scheme. Finally, the applicability of the proposed novel ADP-based decentralized SMC strategy is illustrated by a two-machine power system subject to three different injection attacks.
This brief studies the stability of switched systems in which all the subsystems may be unstable. In addition, some of the switching behaviors of the systems are destabilizing. By using the piecewise Lyapunov function method and taking a tradeoff between the increasing scale and the decreasing scale of the Lyapunov function at switching times, the maximum dwell time for admissible switching signals is obtained and the extended stability results for switched systems in a nonlinear setting are first derived. Then, based on the discretized Lyapunov function method, the switching stabilization problem for linear context is solved. By contrasting with the contributions available in the literature, we do not require that all the switching behaviors of the switching system under consideration are stabilizing. More specifically, even if all the subsystems governing the continuous dynamics are not stable and some of the switching behaviors are destabilizing, the stability of the switched system can still be retained. A numerical example is given to illustrate the validity of the proposed results.
This paper investigates the delay-dependent adaptive synchronization problem of the master and slave structure of linear systems with both constant neutral and time-varying discrete time-delays and nonlinear perturbations based on the Barbalat lemma and matching conditions. An adaption law which includes the master-slave parameters is obtained by using the Lyapunov functional method and inequality techniques to synchronize the master-slave systems without the knowledge of upper bounds of perturbation terms. Particularly, it is shown that the synchronization speed can be controlled by adjusting the update gain of the synchronization signal. A numerical example has been given to show the effectiveness of the method.
This paper presents an application of wavelet networks in identification and control design for a class of non-linear dynamical systems. The technique of feedback linearization, supervisory control and H∞ control are used to design an adaptive control law and also the parameters of the wavelet networks are adapted using a Lyapunov-based design. By some theorems, it will be proved that even in the presence of modelling errors, named network error, the stability of the overall identification scheme and the convergence of the network parameters and the boundedness of the state errors are guaranteed.
In this paper, the problem of Soft-stall power control design for a small wind turbine is considered. Passive stalling and furling methods are widely used to limit the output power of small wind turbines at above-rated wind speed conditions. However, these methods have substantial limitations, for instance, related to tracking the maximum power at some wind speed levels, limited variable speed operation and introducing unbalanced forces on wind turbine blades. Soft-stall power control is a promising technique to overcome above limitations and improve the performance of small wind turbines. Small wind turbines have a comparatively low moment of inertia value, and it is possible to make fast speed changes by generator torque control which is essential to the successful implementation of proposed control method. A sliding-mode controller is developed as the wind turbine speed controller. Furthermore, two sliding-mode current controllers are utilized in the field oriented control system of the generator. A simulation study is illustrated to validate the proposed soft-stall power control technique and results are compared with two other speed control strategies which confirm the applicability of the proposed control technique for small wind turbines.
No abstract is provided for this article.
In recent times we have seen a trend of having the ideologies of the two dominant political parties in the U.S. growing further and further apart. Simultaneously we have entered the age of big data raising enormous interest in computational approaches to solve problems in many domains such as political elections. However, an overlooked problem lies in predicting what happens once our elected officials take office, more specifically, predicting the congressional votes, which are perhaps the most influential decisions being made in the U.S. This, nevertheless, is far from a trivial task, since the congressional system is highly complex and heavily influenced by both ideological and social factors. Thus, dedicated efforts are required to first effectively identify and represent these factors, then furthermore capture the interactions between them. To this end, we proposed a robust end-to-end framework Multi-Factor Congressional Vote Prediction (MFCVP) that defines and encodes features from indicative ideological factors while also extracting novel social features. This allows for a principled expressive representation of the complex system, which ultimately leads to MFCVP making accurate vote predictions. Experimental results on a dataset from the U.S. House of Representatives shows the superiority of MFCVP to several representatives approaches when predicting votes for individual representatives and also the overall outcome of the bill voted on. Finally, we perform a factor analysis to understand the effectiveness and interplay between the different factors.
In this paper, a mathematical model for vehicle-to-vehicle frontal crash is developed. The experimental data are taken from the National Highway Traffic Safety Administration. To model the crash scenario, the two vehicles are represented by two masses moving in opposite directions. The front structures of the vehicles are modeled by Kelvin elements, consisting of springs and dampers in parallel, and estimated as piecewise linear functions of displacements and velocities, respectively. To estimate and optimize the model parameters, a genetic algorithm approach is proposed. Finally, it is observed that the developed model can accurately reproduce the real kinematic results from the crash test.
To satisfy the increasing demands of the accuracy for the human localization, in this work, we propose a pedestrian tracking method by tightly coupling recent inertial navigation system–based and ultra-wideband–based measurements. In this mode, the difference between the distances derived from the inertial navigation system–based and ultra-wideband–based system is used as the observation of the data fusion filter. Moreover, in order to improve the performance of the extended finite impulse response filter, which depends on the averaging horizon ([Formula: see text]) when the error state vector ([Formula: see text]) is determined due to the model, the extended finite impulse response filter bank is employed to be the fusion center for pedestrian tracking, which used the Mahalanobis distance to find the optimal [Formula: see text] at each time index [Formula: see text]. Test experiments illustrate that the extended finite impulse response filter bank–based tightly coupled inertial navigation system/ultra-wideband–integrated method is able to achieve real-time estimation, and its accuracy is similar to the extended finite impulse response with the ideal [Formula: see text] which is calculated off-line.
The problem of event-triggered neural adaptive fault-tolerant finite-time control is investigated for a class of nonstrict feedback nonlinear systems in the presence of nonaffine nonlinear faults. The event-triggered signal is designed by using a relative-threshold to reduce communication burden. The dynamic surface control method is used to relax the assumption of the reference signal and deal with the computational complexity issue. Based on the finite-time stability, a new neural adaptive backstepping design method is developed. The event-triggered neural adaptive fault-tolerant control law is developed for the closed-loop system so that not only the semi-global practical finite-time stability is ensured, but also the tracking performance with a small residual set is guaranteed. Finally, the effectiveness of the proposed control law is verified via simulation results.
The aim of this article is to develop a soft approach for a real-time cell temperature prediction in the aluminum electrolysis reduction. Under the limited labeled data constraint, Laplacian semi-supervised learning methods, which can fully utilize the underlying structure of the data distribution and further extract information contained in all available data, has recently received extensive attention in the field of soft sensor modeling. Since the Laplacian underlying manifold is a constant, it remains a challenging task to improve the extrapolating ability for the case that only a few labeled samples are available. This study presents a soft modeling method based on a semi-supervised deep learning structure, which was developed from the hierarchical autoencoders with extreme learning machine. Furthermore, a Laplacian–Hessian semi-supervised extreme learning machine is built to learn all the geometric distribution information. The Laplacian–Hessian semi-supervised extreme learning machine method is applied to estimate the cell temperature in an aluminum reduction process. The experimental results demonstrate the performance and robustness of the proposed algorithm are superior to those of the existing state-of-the-art methods.
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 learningbased 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.
Snake-like robots can imitate the movement patterns of animals in nature and enter the space that traditional robots cannot enter, which adapt to environments that humans cannot reach, and expand the field of human exploration. However, it is often challenging to realize autonomous navigation and simultaneously avoid obstacles under an unknown environment, that is, active SLAM (Simultaneous Localization and Mapping). This paper proposes an autonomous obstacle avoidance method combined with SLAM based on deep reinforcement learning for a wheeled snake robot by using a multi-sensor. Firstly, we design a modular wheeled snake robot structure with lightweight materials based on orthogonal joints and build a three-dimensional model of a snake robot in Gazebo. Secondly, the SLAM based on two-dimensional LiDAR and IMU is used to realize autonomous navigation under an unknown environment and detect obstacles. At the same time, a Deep Q-Learning-based path planning method of the snake robot is proposed to realize obstacles avoidance during navigation. Finally, simulation studies and experiments show that the designed snake-like robot can realize effective path planning and environmental mapping in environments with obstacles. The proposed active SLAM algorithm improves the success rate of snake-like robot path planning, has better obstacle avoidance ability for obstacles, and reduces the number of collisions compared with the traditional A* and the sampling-based RRT* algorithms.
This paper investigates the electrically operated pitch systems of offshore wind turbines for online condition monitoring and health assessment. The current signature based fault diagnostics is developed for electrically operated pitch systems using model-based approach. The electrical motor faults are firstly modelled based on modified winding function theory and then, current signature analysis is performed to detect the faults. Further, in order to verify the fault diagnostics capabilities in realistic conditions, the operating profiles are obtained from FAST simulation of offshore wind turbines in various wind conditions. In this way, the applicability of current signature analysis for fault diagnostics in offshore wind turbine pitch systems is demonstrated.
This paper is devoted to study the Razumikhin stability theorem for a class of impulsive stochastic delay differential systems. By developing a new lemma, stochastic analysis technique, and Razumikhin approach, several novel criteria of the pth moment exponential stability are derived for the related systems. The key feature of the criteria is that time-derivatives of the Razumikhin functions are allowed to be indefinite, which loosens the constraints of the existing results greatly. Finally, two examples are given to illustrate the usefulness and significance of the theoretical results.
