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The attitude control of an underactuated reusable launch vehicle (RLV) in the reentry phase involving nonminimum phase problem and control input constraints is investigated in this article. To address the nonminimum phase problem, an approach combining output redefinition and robust backstepping is proposed, where a synthetic output is constructed using the combination of the original output and the internal states to obtain stable zero dynamics, and then robust backstepping is performed on the new output. Besides, the ideal internal dynamics are obtained by using optimal bounded inversion, which are incorporated into the controller as the reference trajectories for the internal states to improve the output tracking accuracy. To cope with the control input constraints, a simple and useful anti-windup strategy is proposed by using feedback error clipping, which is shown to be very effective in mitigating control input saturation. Numerical simulations are given to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Although the literature on the a priori procedure, designed to help researchers determine the sample sizes they should use in their substantive research, is expanding rapidly, there are two important limitations. First, there is a need to expand to new popular distributions, log-normal and gamma distributions, and the present work provides those expansions. Second, there is a need to test the consequences of wrong distributional assumptions; for example, assuming a log-normal distribution when the population follows a gamma distribution, or the reverse. The present work addresses the limitations with respect to estimating population means, and it includes computer simulations, links to free and user-friendly programs that researchers can utilize for their own research, and two examples involving real data sets for illustrations of our main results. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the robust nonlinear control of friction-induced vibrations (FIV), more particularly those generated according to the mode-coupling mechanism. A nonlinear scheme which consists of a sliding-mode controller implemented by using a high-gain state observer is proposed. The main objective is to suppress or mitigate the generated vibrations by taking into account of the nonlinearities and uncertainties inherent to friction systems. Hence, this study proposes the analysis of the closed-loop performances of the high-gain observer-based sliding-mode controller when used for the active control of vibrations issued from the mode-coupling mechanism. Based on numerical simulations, the proposed controller has shown suitable performances distinguished from an effective suppress of the generated vibrations. Otherwise, it is shown that the gain of the used nonlinear state observer must be tuned in order to ensure a suitable compromise between the robustness level of the performances with respect to parameter uncertainty and the robustness level with respect to the measurement noise. [ABSTRACT FROM AUTHOR]

Abstract A concept of bypass rewiring on directed networks is proposed, and random bypass rewiring on infinite directed random networks is analytically and numerically investigated with double generating function formalisms and simulations. As a result, it is derived that random bypass rewiring makes infinite directed (undirected) random networks extremely robust for arbitrary occupation probabilities if and only if in-degree of every node except a fixed number of nodes is equal to the out-degree (every node except a finite number of nodes has even degree); random bypass rewiring can make the percolation threshold 0 on infinite directed (undirected) random networks. From the results on infinite random networks, it is generalized that a finite network has a strongly connected spanning subnetwork which has an Eulerian path or cycle if and only if there exists an way of bypass rewiring to make the finite network extremely robust for every combination of removed nodes; Eulerian networks are extremely robust with bypass rewiring for every combination of removed nodes. The generalized results say that bypass rewiring improves connectivity and robustness of not only infinite networks but also real-world networks, like the Internet, with a finite number of nodes. Highlights • A concept of bypass rewiring on directed networks is proposed. • Random bypass rewiring can guarantee extreme robustness of random networks. • Bypass rewiring can make the percolation threshold 0. • Bypass rewiring guarantees extreme robustness of Eulerian networks. • Bypass rewiring can guarantee extreme robustness of the Internet topology. [ABSTRACT FROM AUTHOR]

In this article, we have proposed a methodology for making a radial basis function network (RBFN) robust with respect to additive and multiplicative input noises. This is achieved by properly selecting the centers and widths for the radial basis function (RBF) units of the hidden layer. For this purpose, firstly, a set of self-organizing map (SOM) networks are trained for center selection. For training a SOM network, random Gaussian noise is injected in the samples of each class of the data set. The number of SOM networks is same as the number of classes present in the data set, and each of the SOM networks is trained separately by the samples belonging to a particular class. The weight vector associated with a unit in the output layer of a particular SOM network corresponding to a class is used as the center of a RBF unit for that class. To determine the widths of the RBF units, p-nearest neighbor algorithm is used class-wise. Proper selection of centers and widths makes the RBFN robust with respect to input perturbation and outliers present in the data set. The weights between the hidden and output layers of RBFN are obtained by pseudo inverse method. To test the robustness of the proposed method in additive and multiplicative noise scenarios, ten standard data sets have been used for classification. Proposed method has been compared with three existing methods, where the centers have been generated in three ways: randomly, using k-means algorithm, and based on SOM network. Simulation results show the superiority of the proposed method compared to those methods. Wilcoxon signed-rank test also shows that the proposed method is statistically better than those methods. [ABSTRACT FROM AUTHOR]

In this work, an output feedback controller is designed based on H-infinity control theory to reduce the influence of parameter perturbation on the performance and robustness of active filters. The designed controller maintained the power current distortion rate below 2.9%, thus guaranteeing the stability of the active filter when parameter perturbation occurred. The simulation results demonstrated the new controller's improved compensation performance and robustness compared to traditional hysteresis control. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to solve the position tracking control problem for the permanent magnet linear motor by using the discrete-time fast terminal sliding mode control (SMC) method. Specifically, based on Euler's discretization technique, the approximate discrete-time model is first obtained and analyzed. Then, by introducing a new type of discrete-time fast terminal sliding surface, an improved discrete-time fast SMC method is developed and an equivalent-control-based fast terminal SMC law is subsequently designed. Rigorous analysis is provided to demonstrate that the fast terminal SMC law can offer a higher accuracy than the traditional linear SMC law. Numerical simulations and experimental results are finally performed to demonstrate the effectiveness of the proposed approach and show the advantages of the present discrete-time fast terminal SMC approach over some existing approaches, such as discrete-time linear sliding mode control approach and the PID control method. [ABSTRACT FROM AUTHOR]

This paper addresses formation control with obstacle avoidance problem for a class of second-order stochastic nonlinear multiagent systems under directed topology. Different with deterministic multiagent systems, stochastic cases are more practical and challenging because the exogenous disturbances depicted by the Wiener process are considered. In order to achieve control objective, both the leader-follower formation approach and the artificial potential field (APF) method are combined together, where the artificial potential is utilized to solve obstacle avoidance problem. For obtaining good system robustness to the undesired side effects of the artificial potential, $H_\infty$ analysis is implemented. Based on the Lyapunov stability theory, it is proven that control objective can be achieved, of which obstacle avoidance is proven by finding an energy function satisfying that its time derivative is positive. Finally, a numerical simulation is carried out to further demonstrate the effectiveness of the proposed formation schemes. [ABSTRACT FROM PUBLISHER]

We present a novel and efficient approach for robust estimation of displacement in real-time strain imaging for freehand ultrasound elastography by utilizing pre- and post-deformation ultrasound images. We define a quality factor for image lines and find the line with the highest value of quality factor to serve as the seed line for generating the displacement map. We also develop an analytical framework for coarse-to-fine displacement estimation, obtain an initial estimate of the seed line’s displacement with subsample precision, and propagate it to the entire image to obtain a high quality strain image. Our fast strategy for estimating the seed line’s displacement enables us to enhance the robustness without sacrificing the speed by identifying a new seed line when the quality falls below a given threshold. This is more efficient than the existing approaches that utilize multiple seed lines to improve robustness. Simulations, phantom experiments, and clinical studies show high signal-to-noise-ratio and contrast-to-noise-ratio values in our method for a wide range of average strain levels (1%–10%). Phantom experiments also demonstrate that our method is robust against corrupt and decorrelated data. Our method is superior to the existing real-time methods as it can produce high-quality strain images for up to 10% average strain levels at the rate of 20 frames/s on conventional CPUs. [ABSTRACT FROM AUTHOR]

Cascade phenomenon may lead to catastrophic disasters which extremely imperil the network safety or security in various complex systems such as communication networks, power grids, social networks and so on. In some flow-based networks, the load of failed nodes can be redistributed locally to their neighboring nodes to maximally preserve the traffic oscillations or large-scale cascading failures. However, in such local flow redistribution model, a small set of key nodes attacked subsequently can result in network collapse. Then it is a critical problem to effectively find the set of key nodes in the network. To our best knowledge, this work is the first to study this problem comprehensively. We first introduce the extra capacity for every node to put up with flow fluctuations from neighbors, and two extra capacity distributions including degree based distribution and average distribution are employed. Four heuristic key nodes discovering methods including High-Degree-First (HDF), Low-Degree-First (LDF), Random and Greedy Algorithms (GA) are presented. Extensive simulations are realized in both scale-free networks and random networks. The results show that the greedy algorithm can efficiently find the set of key nodes in both scale-free and random networks. Our work studies network robustness against cascading failures from a very novel perspective, and methods and results are very useful for network robustness evaluations and protections. [ABSTRACT FROM AUTHOR]

In this paper, the performance of metro networks is studied from a network science perspective. We review the structural efficiency of metro networks on the basis of a passenger’s intuitive routing strategy that optimizes the number of transfers and the distance traveled.A new node centrality measure, called node occupying probability , is introduced for evaluating the level of utilization of stations. The robustness of a metro network is analyzed under several attack scenarios. Six metro networks (Beijing, London, Paris, Hong Kong, Tokyo and New York) are compared in terms of the node occupying probability and a few other performance parameters. Simulation results show that the New York metro system has better topological efficiency, the Tokyo and Hong Kong systems are the most robust under random attack and target attack , respectively. [ABSTRACT FROM AUTHOR]

Summary: This paper describes a new robust model predictive control (MPC) scheme to control the discrete‐time linear parameter‐varying input‐output models subject to input and output constraints. Closed‐loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal set, which are solved offline, for the underlying online MPC optimization problem. The main attractive feature of the proposed scheme in comparison with previously published results is that all offline computations are now based on the convex optimization problem, which significantly reduces conservatism and computational complexity. Moreover, the proposed scheme can handle a wider class of linear parameter‐varying input‐output models than those considered by previous schemes without increasing the complexity. For an illustration, the predictive control of a continuously stirred tank reactor is provided with the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a reinforced gradient-type iterative learning control algorithm is developed for a type of discrete linear time-invariant systems with parameters uncertainties and external noises. The technique is to construct a symmetric learning gain matrix on basis of the system Markov parameters and an appropriate learning step length. First, for the case when both the model uncertainties and the external noises are absent, sufficient and necessary monotone convergences of the proposed algorithm are derived by means of matrix theory and norm inequality under the assumption that the learning step length is properly chosen. Then, for the cases when the model uncertainties are tolerable and the external noises are bounded, the robust monotone convergence and robustness are respectively analysed. Compared with the conventional gradient-type iterative learning control scheme, the proposed reinforced one is more efficient in speeding up the convergent tracking performance and resisting perturbations. Numerical simulations testify the validity and the effectiveness as well as the feasibility. [ABSTRACT FROM AUTHOR]

In this paper, we consider the optimal design of interlinks for an interdependent system of networks. In contrast to existing literature, we explicitly exploit the information of intra-layer node degrees to design interdependent structures such that their robustness against cascading failures, triggered by randomized attacks, is maximized. Utilizing percolation theory-based system equations relating the robustness of the network to its degree sequence, we characterize the optimal design for the one-to-one structure, with complete interdependence and partial interdependence, under randomized attack. We also extend our study to the one-to-many interdependence structure and the targeted attack model. The theoretically derived optimal interdependence structures have been verified using simulations on scale-free networks. [ABSTRACT FROM AUTHOR]

A novel robust Student's t-based Kalman filter is proposed by using the variational Bayesian approach, which provides a Gaussian approximation to the posterior distribution. Simulation results for a manoeuvring target tracking example illustrate that the proposed filter has smaller root mean square error and bias than existing filters. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of quickly detecting an unknown change in the conditional densities of a dependent stochastic process. In contrast to the existing quickest change detection approaches for dependent stochastic processes, we propose minimax robust versions of the popular Lorden, Pollak, and Bayesian criteria for when there is uncertainty about the parameter of the post-change conditional densities. Under an information-theoretic Pythagorean inequality condition on the uncertainty set of possible post-change parameters, we identify asymptotic minimax robust solutions to our Lorden, Pollak, and Bayesian problems. Finally, through simulation examples, we illustrate that asymptotically minimax robust rules can provide detection performance comparable to the popular (but more computationally expensive) generalized likelihood ratio rule. [ABSTRACT FROM PUBLISHER]

Phase equilibrium calculations converge less often as the number of phases and components increase, and for overall compositions closer to phase split boundaries and critical points. Computational speed and robustness of flash calculations are very important aspects for pipeline transmission and for reservoir simulation where billions of flash calculations may be done. Reduced methods have the potential to improve the robustness of phase equilibrium calculations because if chosen properly they can linearize the equations, use fewer independent variables and can be unbounded. Improved robustness could further improve the speed and accuracy of compositional simulation and avoid false two-phase solutions, where three or more phases may be present. In this paper, we use the reduced variables of Gorucu and Johns (2014) to test robustness in performing two- and three-phase stability analysis and corresponding flash calculations. These multi-phase equilibrium calculations are compared with the conventional phase equilibrium calculations based on minimization of Gibbs energy and the reduced method proposed by Okuno et al. (2010a) . Using thousands of equally-spaced and unbiased multi-phase equilibrium calculations, the proposed multi-phase equilibrium calculations are shown to be more robust than the other two tested algorithms. [ABSTRACT FROM AUTHOR]

The lane keeping problem of automatic driving for vehicles was studied based on the simplified linear lateral dynamic model which was divided into a translation subsystem and a rotation subsystem. The rotation subsystem was defined as an inner loop and the translation subsystem was defined as an outer loop. With the separation design strategy of inner loop and outer loop, a kind of angle stable controller was designed to make the rotation subsystem stable. The inner angle stable subsystem was driven by the outer translation subsystem which was controlled by the error signal of vehicle position. The inner loop was cascaded with outer loop and both loops were designed by the sliding mode method. At last, detailed numerical simulations were done and simulation results were compared with other three methods to explain the physical meanings of design and its three advantages. [ABSTRACT FROM AUTHOR]

Robust fault estimation plays an important role in real-time monitoring, diagnosis, and fault-tolerance control. Accordingly, this paper aims to develop an effective fault estimation technique to simultaneously estimate the system states and the concerned faults, while minimizing the influences from process/sensor disturbances. Specifically, an augmented system is constructed by forming an augmented state vector composed of the system states and the concerned faults. Next, an unknown input observer (UIO) is designed for the augmented system by decoupling the partial disturbances and attenuating the disturbances that cannot be decoupled, leading to a simultaneous estimate of the system states and the concerned faults. In order to be close to the practical engineering situations, the process disturbances in this study are assumed not to be completely decoupled. In the first part of this paper, the existence condition of such an UIO is proposed to facilitate the fault estimation for linear systems subjected to process disturbances. In the second part, robust fault estimation techniques are addressed for Lipschitz nonlinear systems subjected to both process and sensor disturbances. The proposed technique is finally illustrated by the simulation studies of a three-shaft gas turbine engine and a single-link flexible joint robot. [ABSTRACT FROM AUTHOR]

A kind of backstepping sliding mode control law was proposed to solve the lane keeping problem where the automatic driving system was simplified to be a kind of linear lateral dynamic model. And the dynamic response characteristic of tire steering system was described to be a first order system with a proper delay time constant. First, the complex system was divided into a relative accurate subsystem and an uncertain subsystem. Second, the proposed backstepping sliding mode method was proposed to eliminate the lateral position error which can make use of the advantage of sliding mode method that it can improve the robustness of the whole system. The backstepping strategy was adopted for the accurate inertial delay dynamic of tire steering system. Third, a kind of soft function was used to reduce the oscillation caused by sliding mode control. And a Lyapunov function was constructed to guarantee all signals of close loop system are stable. At last, 100 times of random numerical simulations were done to testify the rightness and stability of the proposed method applied in the situation that the tire coefficients are uncertain. [ABSTRACT FROM AUTHOR]

This paper aims to address a new class of multiscale coordination control problems for multivehicle systems that are subject to external disturbances. Each vehicle is measured by an independent scale due to the constraints of physical environments. Based on the nearest neighbor-interaction rules, three algorithms of different convergence rates are presented such that the vehicles are guaranteed to achieve consensus on a common quantity for all but of their own scales. Simulation tests are performed to validate the effectiveness and robustness of these consensus algorithms. By introducing a Lyapunov potential function, the consensus analysis is further achieved for the three multiscale algorithms, regardless of them with an exponential, finite-time, or fixed-time convergence rate and in the presence of bounded disturbances. [ABSTRACT FROM PUBLISHER]

We present a non-parametric affine-invariant test for the multivariate Behrens–Fisher problem. The proposed method based on the spatial medians is asymptotic and does not require normality of the data. To improve its finite sample performance, we apply a correction of the type which was already used in a similar test based on trimmed means, however, our simulations show that in the case of heavy-tailed distributions our method performs better. Also in a simulation comparison with a recently published rank-based test our test yields satisfactory results. [ABSTRACT FROM PUBLISHER]

Interdependent network models are often used to show how one network has an effect on another network through dependencies. In this paper, we propose a novel interdependent network model which consists of two individual networks with unequal numbers of nodes and one-to-multiple weighted dependency links between the two networks. Based on realistic assumptions, this model differs from previous works that considered equal numbers of nodes in the two networks and identical dependency links. We formulate an optimization problem to allocate dependency links using least resources. This novel model enhances the practicability of traditional cyber–physical system structures, but it makes the dependency link deployment problem more complex and the optimization problem cannot be solved in large networks. To overcome this problem, we propose a new algorithm based on a revised network flow method. Extensive simulations on random networks and real networks show that our deployment method produces topologies that are more robust than the ones obtained by other deployment techniques. Results indicate that our algorithm is efficient and cost-effective in designing robust interdependent networks, and our deployment method is suitable for networks of any size. [ABSTRACT FROM AUTHOR]

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the cyber-physical system consisting of interdependent physical-resource and computational-resource networks, e.g., smart power grids, automated traffic control system, and wireless sensor and actuator networks, where the physical-resource and computational-resource network are connected and mutually dependent. The failure in physical-resource network might cause failures in computational-resource network, and vice versa. A small failure in either of them could trigger cascade of failures within the entire system. We aim to investigate the issue of cascading failures occur in such system. We propose a typical and practical model by introducing the interdependent complex network. The interdependence between two networks is practically defined as follows: Each node in the computational-resource network has only one support link from the physical-resource network, while each node in physical-resource network is connected to multiple computational nodes. We study the effect of cascading failures using percolation theory and present detailed mathematical analysis of failure propagation in the system. We analyze the robustness of our model caused by random attacks or failures by calculating the size of functioning parts in both networks. Our mathematical analysis proves that there exists a threshold for the proportion of faulty nodes, above which the system collapses. Using extensive simulations, we determine the critical values for different system parameters. Our simulation also shows that, when the proportion of faulty nodes approaching critical value, the size of functioning parts meets a second-order transition. An important observation is that the size of physical-resource and computational-resource networks, and the ratio between their sizes do not affect the system robustness. [ABSTRACT FROM AUTHOR]

The robustness of a hybrid separation process where the performance of mixed-recycle steady state recycling chromatography is enhanced by integrating it with a solvent removal unit is analysed theoretically and by means of numerical simulations. The equilibrium theory of chromatography is applied to derive equations for boundaries of feasible operating parameters in such a hybrid process. Visualization of the feasible operating parameter ranges helps in analysing the influence of various physical and process parameters of robustness of an operating point. It is observed that process robustness can be improved by adjusting the solvent removal capacity or the cut times. When the solvent removal capacity increases, the region of feasible cut times becomes narrower due to increased non-linearity of the system. This makes it more difficult to maintain robust operation. [ABSTRACT FROM AUTHOR]

This article investigates transient and steady state performance of sliding mode control using fractional calculus and fuzzy logic system. Sliding mode control is robust to uncertainties but its major disadvantage is severe chattering. To overcome the problem, boundary layer design is used at the cost of loss of robustness. Steady state performance is guaranteed by introducing integral sliding mode control (ISMC); however, the transient response degrades. This work proposes a new sliding mode control method using fractional operator and fuzzy logic system which recovers transient performance and ensures steady performance with minimum chattering. Fuzzy logic system is used to adjust the order of fractional operator. The proposed method is derived for position control of servo system which is subjected to nonlinear disturbance. Numerical simulations confirm superiority of proposed method. [ABSTRACT FROM AUTHOR]

ABSTRACT A compact dual-frequency rectenna has been developed. The receiving antenna is based on a planar inverted F antenna for dual frequency operation. A voltage doubler is proposed to improve the output voltage and the robustness of the rectenna. The simulation and measurement results validate the performance of the rectenna. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:319-322, 2015 [ABSTRACT FROM AUTHOR]

A parameterised design of robust composite nonlinear controller is proposed for typical second-order servo systems subject to unknown constant disturbance and control input saturation. The control law consists of a linear feedback part for achieving fast response, a nonlinear feedback part for suppressing the overshoot, and a disturbance-compensation mechanism for erasing the steady-state error. An extended state observer is adopted to estimate the unknown disturbance. The closed-loop stability is analysed theoretically. The control scheme is applied to the speed regulation of permanent magnet synchronous motor, and numerical simulations are carried out. The results confirm that the proposed control scheme can achieve fast, smooth, and accurate speed regulation, and has a certain degree of robustness with respect to the amplitude of disturbances and the perturbations of system parameters. [ABSTRACT FROM AUTHOR]

Sure independence screening (SIS) has been proposed to reduce the ultrahigh dimensionality down to a moderate scale and proved to enjoy the sure screening property under Gaussian linear models. However, the observed response is often skewed or heavy-tailed with extreme values in practice, which may dramatically deteriorate the performance of SIS. To this end, we propose a new robust sure independence screening (RoSIS) via considering the correlation between each predictor and the distribution function of the response. The proposed approach contributes to the literature in the following three folds: First, it is able to reduce ultrahigh dimensionality effectively. Second, it is robust to heavy tails or extreme values in the response. Third, it possesses both sure screening property and ranking consistency property under milder conditions. Furthermore, we demonstrate its excellent finite sample performance through numerical simulations and a real data example. [ABSTRACT FROM AUTHOR]

We discuss robust M-estimation of INARCH models for count time series. These models assume the observation at each point in time to follow a Poisson distribution conditionally on the past, with the conditional mean being a linear function of previous observations. This simple linear structure allows us to transfer M-estimators for autoregressive models to this situation, with some simplifications being possible because the conditional variance given the past equals the conditional mean. We investigate the performance of the resulting generalized M-estimators using simulations. The usefulness of the proposed methods is illustrated by real data examples. [ABSTRACT FROM AUTHOR]

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing data with an arbitrary abnormal component. Under this model, we then analyze the impact of general abnormal data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal component pursuit, and develop a data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal data parameter configurations. [ABSTRACT FROM PUBLISHER]

This paper investigates the skid steering of four-wheel independent-drive (4WID) electric vehicles (EV) and a differential steering of a 4WID EV with a steer-by-wire (SBW) system in case of steering failure. The dynamic models of skid steering vehicle (SSV) and differential steering vehicle (DSV) are established and the traditional front-wheel steering vehicle with neutral steering characteristics is selected as the reference model. On this basis, sideslip angle observer and two different sliding mode variable structure controllers for SSV and DSV are designed respectively. Co-simulation results of CarSim and Simulink show that the designed controller for DSV not only controls the yaw rate and sideslip angle of DSV to track those of the reference model exactly, but also ensures the robustness of the controlled system compared with the designed controller for SSV. And the differential driving torque needed to realize the differential steering is much smaller than that for skid steering, which indicates the possibility of the differential steering in case of steering failure. [ABSTRACT FROM AUTHOR]