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This article deals with the distributed $\mathcal {H}_{\infty }$ robust control problem for linear multiagent systems perturbed by external disturbances and norm-bounded uncertain parameters over the Markovian randomly switching communication topologies. To tackle this problem, the distributed observer-based controller is proposed, which requires the relative information between neighbors and the absolute information of a subset of the nodes, and thus is intrinsically distributed. It is of great interest to see that the distributed $\mathcal {H}_{\infty }$ robust control problem governed by such a controller can be converted to the stabilization with $\mathcal {H}_{\infty }$ disturbance attenuation problems of some decoupled linear systems, whose dimensions equal those of a single node. Then, the $\mathcal {H}_{\infty }$ stochastic robust performance region is defined to indicate the robustness of this controller against the variation of communication topologies. It is theoretically shown that the distributed observer-based controller yields bounded and connected robust performance region. Finally, the theoretical results are verified by conducting numerical simulations and experiments. [ABSTRACT FROM AUTHOR]

In this article, we apply the active disturbance rejection control (ADRC) to the output tracking of a class of lower triangular nonlinear systems subject to mismatched bounded stochastic disturbances of unknown statistic characteristics and nonvanishing at the origin. A major obstacle is that the paths of the stochastic disturbances are nowhere differentiable almost surely which causes that the stochastic disturbances cannot be refined into the control input channel by the usual way of state transformation. To overcome this obstacle, a set of second-order extended state observers is first designed to estimate, in real time, the disturbance in each channel, and then a backstepping ADRC based on feedforward compensation and a constructive backstepping procedure is developed, guaranteeing that the closed-loop output tracks a time-varying reference signal in practically mean square and the closed-loop states are practically bounded in probability first defined in this article. Finally, some numerical simulations are presented to validate the effectiveness of the proposed backstepping ADRC approach. [ABSTRACT FROM AUTHOR]

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]

In this article, a composite disturbance rejection control method is proposed for attitude control of quadrotor using an inner–outer loop control framework, which can effectively improve the angular tracking performance of the quadrotor and achieve the active disturbance rejection. In the inner loop, a novel nonlinear extended state observer (NESO) is designed to estimate the disturbance and nonlinear part of the system model, then a robust control law is embedded in inner-loop controller to attenuate the effect of the estimation error. In the outer loop, a composite nonlinear feedback (CNF) controller is designed to improve the transient performance. Moreover, the stability of two loops are also analyzed, respectively. Numerical simulation and platform experiment are carried out to verify the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Considering that wireless unidirectional full-duplex (FD) transmission is one of the FD transmissions over 5G mobile wireless networks, this paper proposes a joint receive and transmit relay beamformer design for systems consisting of a source, a FD relay, an intended destination, and an eavesdropper. The source and destination are single-antenna nodes, whereas the FD relay is a multi-antenna node. Assuming that full channel state information (CSI) is available and the eavesdropper processes two blocks of received data at a time, the joint optimization problem is reformulated as a semidefinite relaxation (SDR) problem (in terms of relay transmit beamformer), which is tight in the sense that the rank-one beamformer can always be recovered from the SDR solution. In order to significantly minimize the computational cost of the SDR implementation, a novel semi-analytical approach based on dual optimization is proposed to obtain the optimum transmit beamformer. When the number of data blocks is arbitrary, we propose a successive convex approximation approach for solving the original non-convex optimization problem and show that a stationary solution can be efficiently obtained. We then propose a distributionally robust beamformer design for the case of an arbitrary number of blocks, assuming that the means and covariance matrices of the eavesdropper's channels are known. Computer simulations show that the proposed semi-analytical solution matches with that obtained from numerical optimization of the SDR problem, the FD scheme significantly outperforms its half-duplex counterpart, and the proposed robust design provides robustness against CSI uncertainty. [ABSTRACT FROM AUTHOR]

This paper has a twofold goal. The first aim is to provide a deeper understanding of the family of the real elliptically symmetric (RES) distributions by investigating their intrinsic semiparametric nature. The second aim is to derive a semiparametric lower bound for the estimation of the parametric component of the model. The RES distributions represent a semiparametric model, where the parametric part is given by the mean vector and by the scatter matrix, while the non-parametric, infinite-dimensional, part is represented by the density generator. Since, in practical applications, we are often interested only in the estimation of the parametric component, the density generator can be considered as nuisance. The first part of this paper is dedicated to conveniently place the RES distributions in the framework of the semiparametric group models. In the second part, building on the mathematical tools previously introduced, the constrained semiparametric Cramér–Rao bound (CSCRB) for the estimation of the mean vector and of the constrained scatter matrix of a RES distributed random vector is introduced. The CSCRB provides a lower bound on the mean squared error of any robust $M$ -estimator of mean vector and scatter matrix when no a priori information on the density generator is available. A closed-form expression for the CSCRB is derived. Finally, in simulations, we assess the statistical efficiency of the Tyler's and Huber's scatter matrix $M$ -estimators with respect to the CSCRB. [ABSTRACT FROM AUTHOR]

This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents, and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) within polynomial time. The algorithm is simple and executable based on local interactions with neighbor agents under a strongly connected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of suboptimality of the outcome, and additionally show that at least 50% of suboptimality can be guaranteed if social utilities are nondecreasing functions with respect to the number of coworking agents. The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation. [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]

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]

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]

The single-event upset (SEU) sensitivity of ultra-thin silicon on insulator (SOI), SOI FinFET, and NanoWireFET static random access memories (SRAMs) is investigated using a straightforward multiple-scale approach based on open source and commercial codes. Both Monte-Carlo and technology computer aided design (TCAD) tools are used to estimate the SEU cross section of innovative technologies. Heavy-ion experiments performed on transistors are used to validate TCAD models and 3-D device structures. TCAD simulations are then performed on 3-D SRAM cells to calculate the upset threshold for each technology. It is used as upset criterion in Monte-Carlo simulations of deposited energy in silicon microvolumes to estimate the SEU cross section of innovative SOI technologies. Finally, multiple-bit upsets are addressed to draw trends on the sensitivity of such highly scaled technologies to multiple events. [ABSTRACT FROM PUBLISHER]

The connection of a distributed generation (DG) unit to a weak power system is challenging due to stability issues resulted from dynamic interactions between the DG unit and the grid. An LCL-based DG unit is a particularly challenging case due to the presence of a high resonant peak in its frequency response. This paper proposes a robust control strategy to overcome the stability issues of an LCL-based DG unit connected to a weak grid. The main advantage of the proposed control strategy is that it guarantees stability and satisfactory transient performance against the variations of grid impedance. Moreover, it is able to decouple the $d$ and $q$ channels of the control system, which enables independent regulation of the real and reactive output power of the DG unit. Real-time simulations and experimental tests illustrate the effectiveness of the proposed controller in terms of improved transient performance, robust stability, and satisfactory controller set-point tracking. [ABSTRACT FROM PUBLISHER]

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]

In this paper, a novel predictive smooth variable structure filter (PSVSF) is presented for attitude synchronization during satellite formation flying (SFF). The strategy is proposed based on variable structure control concept and applies to any nonlinear system with modeling errors. Because the modeling errors in the PSVSF are not restricted to Gaussian noise only, the technique can deal with various kinds of uncertainties, parameter variations, or noises. It also has low computational burden; therefore, it is more general than other nonlinear estimation methods. Sufficient conditions to analyze the stability and characteristics of the PSVSF are presented. Simulations are performed to demonstrate the accuracy, robustness, and stability of the proposed PSVSF for the attitude synchronization estimation of the SFF system. [ABSTRACT FROM PUBLISHER]

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]

The degradation in power quality causes adverse economical impact on the utilities and customers. Harmonics in current and voltage are one of the most commonly known power quality issues and are solved by the use of a hybrid series active power filter (HSAPF). In this paper, a new controller design using sliding-mode controller-2 is proposed to make the HSAPF more robust and stable. An accurate averaged model of a three-phase HSAPF is also derived in this paper. The design concept of the robust HSAPF has been verified through simulation and experimental studies, and the results obtained are discussed. [ABSTRACT FROM AUTHOR]

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]

This brief demonstrates a method of real-time motion control for robotic friction stir welding (FSW). For some manufacturing processes, the lack of stiffness of industrial manipulators can cause a lack of precision and is, thus, problematic. During the processes that require significant forces, this error becomes the primary source of defects. This brief provides significant improvements using digital estimators. A compensator based on a discrete-time nonlinear observer and two other compensators that use only motor current and position measurements are proposed to compensate for the tracking error due to the deflection of the robot. Simulations and experiments on an industrial robot show the effectiveness of the three proposed compensators, which successfully attenuate the dynamic error in the case of a 2-D FSW process. Our adapted compensators provide accurate performance ( $\sim 90$ % error reduction) for a robotized FSW welding setup. [ABSTRACT FROM AUTHOR]

The Aumann-type mean has been shown to possess valuable properties as a measure of the location or central tendency of fuzzy data associated with a random experiment. However, concerning robustness its behavior is not appropriate. The Aumann-type mean is highly affected by slight changes in the fuzzy data or when outliers arise in the sample. Robust estimators of location, on the other hand, avoid such adverse effects. For this purpose, this paper considers the M-estimation approach and discusses conditions under which this alternative yields valid fuzzy-valued M-estimators. The resulting M-estimators are applied to a real-life example. Finally, some simulation studies show empirically the suitability of the introduced estimators. [ABSTRACT FROM PUBLISHER]

Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. In this paper, we present a novel approach to such an issue using hidden factor analysis joint sparse representation. In contrast to the majority of tasks in the literature that integrally handle the facial texture, the proposed aging approach separately models the person-specific facial properties that tend to be stable in a relatively long period and the age-specific clues that gradually change over time. It then transforms the age component to a target age group via sparse reconstruction, yielding aging effects, which is finally combined with the identity component to achieve the aged face. Experiments are carried out on three face aging databases, and the results achieved clearly demonstrate the effectiveness and robustness of the proposed method in rendering a face with aging effects. In addition, a series of evaluations prove its validity with respect to identity preservation and aging effect generation. [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]

In the future, large satellites may be automatically assembled on-orbit in order to reduce weight and increase volume efficiency in launchers. During this process, it may be important to maintain satellite attitude. This paper presents a dynamic model, including reaction wheels and a twisting-algorithm-based sliding-mode controller, to maintain attitude during a simple self-assembly process. Simulation results of this controller are compared with results from other controllers found in the literature. [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]

In this paper, a new robust fuzzy H\infty estimator-based stabilization design for a class of $N$ -dimensional nonlinear parabolic partial differential systems (PDSs) with either the Dirichlet or Neumann boundary conditions is proposed. First, an $N$-dimensional parabolic Takagi–Sugeno (T–S) fuzzy PDS is used to approximate the $N$ -dimensional nonlinear parabolic PDS via the knowledge-based T–S fuzzy system technique. Second, based on the $N$-dimensional parabolic T–S fuzzy PDS, a robust fuzzy estimator-based controller is employed not only to stabilize the PDS, but also to attenuate the effects of external and measurement noises on the PDS in the spatiotemporal domain below a prescribed attenuation level. The robust fuzzy H\infty estimator-based stabilization design problem can be formulated as a diffusion matrix inequality (DMI) problem. To solve DMIs via the traditional algebraic matrix techniques, we utilize the divergence theorem and the Poincaré inequality to transform the DMIs into bilinear matrix inequalities with a Poincaré constant defined according to the spatial domain of the corresponding PDS. Finally, the robust fuzzy H\infty estimator-based stabilization design problem can be effectively solved by a set of linear matrix inequalities instead of the BMIs with the help of the proposed decoupled method. Additionally, an optimal robust fuzzy H\infty estimator-based stabilization design can be realized via minimizing the noise attenuation level. A simulation example is provided to illustrate the design procedure and verify the performance of the proposed optimal design. [ABSTRACT FROM PUBLISHER]

This research letter presents the modeling and simulation of electronic differential, employing a novel wavelet controller for two brushless dc motors. The proposed controller uses discrete wavelet transform to decompose the error between actual and reference speed. Error signal that is actually given by the electronic differential based on throttle and steering angle is decomposed into frequency components. Numerical simulation results are provided for both wavelet and proportional-integral-derivate controllers. In comparison, the proposed wavelet control technique provides greater stability and ensures smooth control of the two back driving wheels. [ABSTRACT FROM AUTHOR]

A new Cardiac Electrical Sparse Imaging (CESI) technique is proposed to image cardiac activation throughout the three-dimensional myocardium from body surface electrocardiogram (ECG) with the aid of individualized heart-torso geometry. The sparse property of cardiac electrical activity in the time domain is utilized in the temporal sparse promoting inverse solution, one formulated to achieve higher spatial-temporal resolution, stronger robustness and thus enhanced capability in imaging cardiac electrical activity. Computer simulations were carried out to evaluate the performance of this imaging method under various circumstances. A total of 12 single site pacing and 7 dual sites pacing simulations with artificial and the hospital recorded sensor noise were used to evaluate the accuracy and stability of the proposed method. Simulations with modeling error on heart-torso geometry and electrode-torso registration were also performed to evaluate the robustness of the technique. In addition to the computer simulations, the CESI algorithm was further evaluated using experimental data in an animal model where the noninvasively imaged activation sequences were compared with those measured with simultaneous intracardiac mapping. All of the CESI results were compared with conventional weighted minimum norm solutions. The present results show that CESI can image with better accuracy, stability and stronger robustness in both simulated and experimental circumstances. In sum, we have proposed a novel method for cardiac activation imaging, and our results suggest that the CESI has enhanced performance, and offers the potential to image the cardiac activation and to assist in the clinical management of ventricular arrhythmias. [ABSTRACT FROM AUTHOR]

Robots with elastic joints arouse an increasing interest in the robotics community. Especially, variable stiffness robots promise to be beneficial regarding robustness and task adaptability. However, the control of variable stiffness multijoint robots is challenging due to their highly nonlinear behavior. Here, we present a backstepping approach for tracking control of multijoint variable stiffness robots. To cope with the problem of noisy-state measurements and the need for higher order state derivatives, command filters are used. A stability proof of the complete control approach including filtering is given. Finally, the simulations and measurements on a variable stiffness joint and a multijoint variable stiffness robot validate the approach. [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 performance of bit-patterned media recording is limited, in part, by written-in errors in the write channel, which has recently been modeled by a channel introducing data-dependent insertion, deletion, and substitution (DIDS) errors. The Davey-MacKay (DM) construction has shown promising error performance on the DIDS channel, where synchronization errors are sparse and burst error lengths before and after each synchronization error are short. In this paper, we consider marker codes with a nonbinary low-density parity-check code as the outer code for the DIDS channel. Our contributions are twofold. First, we propose two computationally efficient inner decoding schemes for the DM and marker code constructions. One is suitable when the burst errors in the DIDS channel are short, and the other when the burst errors are long. Second, our computer simulations show that marker codes can increasingly outperform DM codes as the burst error length increases, and thus provide greater robustness against burst errors on the DIDS channel. [ABSTRACT FROM AUTHOR]

Robust design optimization aims to find solutions that are competent and reliable under given uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this paper focuses on problems where uncertainties emanate from design variables. In commercial designs, being reliable is often of more practical value than being globally best (but unreliable). Robust optimization poses three key challenges: 1) appropriate formulation of the problem ; 2) accurate estimation of the “robustness” measure; and 3) efficient means to identify the set of tradeoff robust solutions with an affordable computational cost. In this paper, four different problem formulations for robust optimization are presented and analyzed. The proposed formulations offer a set of tradeoff solutions with robustness from two perspectives—feasibility robustness, i.e., robustness against failure and performance robustness, i.e., robustness assuring good performance. The approach also provides means to identify critical constraints or performance functions that affect the overall robustness. The problem is posed as a many-objective optimization problem and a decomposition-based evolutionary approach is used for solving it. The performance of the proposed approach and the consequences of using different formulations are illustrated using two numerical examples and four engineering problems. [ABSTRACT FROM PUBLISHER]

This brief presents theoretical guarantees for stability and performance for the singular value decomposition (SVD) system with subsystems that are linear and first order. The SVD system reduces the dimension of the control input. It is used to meet the rank-one input constraint imposed by the row–column structure. The row–column structure reduces the number of inputs required to control \mathbf mn subsystems to m+n . Although the subsystems are linear and first order, they can be dynamically coupled and are coupled nonlinearly by the SVD of the control input. Thus, the entire system is of order \mathbf mn and nonlinear. Lyapunov stability and performance analysis demonstrates the effect of the SVD dimension reduction through comparisons to a system with full-rank inputs. The analysis also provides convenient methods for control design. Simulation examples demonstrate the use of the SVD system, theoretical results, and the SVD system’s robustness with respect to noise and nonlinearities. [ABSTRACT FROM PUBLISHER]

The stochastic switching mechanism and intrinsic variability of resistive random access memory (RRAM) present severe challenges for memory applications, which, however, may be utilized to implement the physical unclonable function (PUF) for hardware security. A PUF based on RRAM resistance variability is proposed in this letter. Unlike PUFs based on manufacturing variation, this proposal exploits an intrinsic variability in physical mechanisms with reconfigurability. Key characteristics of the PUF design are assessed by simulation using measured RRAM properties and device model. Truly random variation of RRAM resistance is critical for PUF uniqueness (or unclonability). The reliability (or robustness) of the proposed PUF is affected by temperature and voltage dependence of RRAM resistance as well as retention characteristics. Large separation between inter-chip and intra-chip Hamming distance as the measure of uniqueness and reliability confirms the feasibility of the PUF proposal. [ABSTRACT FROM PUBLISHER]