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Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from the correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors.This prevents one to use automatically established correspondences, which are prone to errors, thereby strongly limiting the scope of NRSfM. We propose a three-step automatic pipeline to solve NRSfM robustly by exploiting isometry. Step 1 computes the optical flow from correspondences, step 2 reconstructs each 3D point's normal vector using multiple reference images and integrates them to form surfaces with the best reference and step 3 rejects the 3D points that break isometry in their local neighborhood. Importantly, each step is designed to discard or flag erroneous correspondences. Our contributions include the robustification of optical flow by warp estimation, new fast analytic solutions to local normal reconstruction and their robustification, and a new scale-independent measure of 3D local isometric coherence. Experimental results show that our robust NRSfM method consistently outperforms existing methods on both synthetic and real datasets., Accepted in TPAMI 2021

Due to the simultaneous existence of model uncertainties and external disturbances, designing automated ground vehicle path-following controllers is recognized as a challenging task. The $H_{∞}$ robust control methodology, as one of the accomplished strategies for controller robustification, has been commonly adopted by researchers to address the vehicle path-tracking problems. Nevertheless, despite its advantages, the $H_{∞}$ controller is only capable of limiting the total ``energy'' of the tracking errors. On the other hand, from a safety standpoint, constraining the ``peak'' of the tracking errors may carry an equal or more importance. To establish a guaranteed upper bound on the path-tracking errors, this paper proposes a novel methodology to synthesis the ground vehicle path-following controller in light of the energy-to-peak robust control theory. Additionally, to address the time-varying uncertainties presented in the tire dynamics, robust stabilization constraints based upon the small-gain theorem are also formulated into the overall controller design problem. Comparative study regarding the disturbance rejection performance between the proposed controller and the conventional $H_{∞}$ approach is conducted via CarSim-Simulink joint simulations. Furthermore, the robustness and disturbance attenuation ability of the energy-to-peak path-tracking controller is experimentally verified on a scaled car.

In this letter, a robust quantized sampled–data controller is provided for a class of nonlinear systems affected by time–varying uncertainties, actuation disturbances and measurement noises. Sufficient conditions based on linear matrix inequalities and ensuring the existence of the proposed robust quantized sampled–data controller are given. Quantization of both state measurements and input signals is simultaneously considered. Input–to–state stability redesign technique is used in order to attenuate the effects of bounded actuation disturbances and of bounded observation errors. It is proved that, under suitably fast sampling and accurate quantization of the input/output channels, the proposed controller achieves the semi–global practical stability, with arbitrarily small final target ball, of the related quantized sampled–data closed–loop system provided that the observation errors do not affect (or affect marginally) the robustification term added in the controller and, that the bounds of the actuation disturbances as well as of the observation errors are a priori known. The theory here developed includes also the cases of time–varying sampling intervals and of non–uniform quantization of the input/output channels as well as the stability analysis of the inter–sampling system behavior. The provided results are validated through an example of one–link manipulator.

We investigate competitive online algorithms for online convex optimization (OCO) problems with linear in-stage costs, switching costs and ramp constraints. While OCO problems have been extensively studied in the literature, there are limited results on the corresponding online solutions that can attain small competitive ratios. We first develop a powerful computational framework that can compute an optimized competitive ratio based on the class of affine policies. Our computational framework can handle a fairly general class of costs and constraints. Compared with other competitive results in the literature, a key feature of our proposed approach is that it can handle scenarios where infeasibility may arise due to hard feasibility constraints. Second, we design a robustification procedure to produce an online algorithm that can attain good performance for both average-case and worst-case inputs. We conduct a case study on Network Functions Virtualization (NFV) orchestration and scaling to demonstrate the effectiveness of our proposed methods.

This article proposes a design approach, based on the integral sliding mode control paradigm, devoted to give robustness to multiagent systems executing arbitrary distributed optimization and consensus protocols which do not take this feature into account. Robustness is understood as the capability of rejecting the effect of exogenous disturbances, parameter uncertainties, and uncertain couplings between the agents dynamics, by achieving the same emerging behavior as that corresponding to a reference multiagent system (MAS) designed to achieve a given coordination objective in the nominal case. The proposed approach yields a distributed state feedback which can seamlessly be integrated into existing distributed optimization and cooperative control protocols which are usually prone to disturbances and uncertainties corrupting the MAS dynamics. Nonsmooth Lyapunov analysis supports the claimed properties. Numerical simulations, showing how popular distributed optimization and consensus protocols can effectively be robustified are discussed.

International audience; In this letter, we consider the robustification of port-Hamiltonian systems with respect to matched time-varying disturbances generated by an exo-system, which is assumed to be known. This letter is an extension, to the case of time-varying disturbances, of recent integral action techniques, which are able to reject constant disturbances only. The approach is then extended to the special case of sinusoidal disturbances with unknown frequency. The main results of this letter are demonstrated on a 2 degree-of-freedom robotic manipulator.

Recently, the robustification of principal component analysis (PCA) has attracted much research attention in numerous areas of science and engineering. The most popular and successful approach is to model the robust PCA problem as a low-rank matrix recovery problem in the presence of sparse corruption. With this model, the nuclear norm and $\ell _{1}$ -norm penalties are usually used for low-rank and sparsity promotion. Although the nuclear norm and $\ell _{1}$ -norm are favorable due to their convexity, they have a bias problem. In comparison, nonconvex penalties can be expected to yield better recovery performance. In this paper, we consider a formulation for robust PCA using generalized nonconvex penalties for low-rank and sparsity inducing. This nonconvex formulation is efficiently solved by a multi-block alternative direction method of multipliers (ADMM) algorithm. A sufficient condition for the convergence of this new ADMM algorithm has been derived. Furthermore, to address the important issue of nonconvex penalty selection, we have evaluated the new algorithm via numerical experiments in various low-rank and sparsity conditions. The results indicate that, “exact” recovery of the low-rank principle component can be achieved only by nonconvex regularization. MATLAB code is available at https://github.com/FWen/RPCA.git .

This work focuses on the robust optimization of a permanent magnet (PM) synchronous machine while considering a driving cycle. The robustification is obtained by considering uncertainties of different origins. Firstly, there are geometrical uncertainties caused by manufacturing inaccuracies. Secondly, there are uncertainties linked to different driving styles. The final set of uncertainties is linked to ambient parameters such as traffic and weather conditions. The optimization goal is to minimize the PM's volume while maintaining a desired machine performance measured by the energy efficiency over the driving cycle and the machine's maximal torque. The magnetic behavior of the machine is described by a partial differential equation (PDE) and is simulated by the finite-element method employing an affine decomposition to avoid reassembling of the system of equations due to the changing PM geometry. The Sequential Quadratic Programming algorithm is used for the optimization. Stochastic collocation is applied to compute moments of stochastic quantities. The robustness of the optimized configurations is validated by a Monte Carlo sampling. It is found that the uncertainties in driving style and road conditions have significant influence on the optimal PM configuration., 5 pages, 4 figures, 2 tables, Compumag 2019 conference

In this paper, two active fault-tolerant control (AFTC) structures are proposed for multiplicative and additive actuator faults respectively. Considering that the traditional generalized internal model control (GIMC) changes the dynamic input-output relationship of the original system, more exact fault information is employed to design the robustification controller of GIMC with the help of fault diagnosis observers. Further, a feedforward compensation unit and a feedback compensation unit are respectively designed for multiplicative and additive faults, striving to approximately recover the system performance to the normal one. Finally, an air handling unit (AHU) system is provided to verify the effectiveness and superiority of the proposed structures.

High-dimensional data present in the real world is often corrupted by noise and gross outliers. Principal component analysis (PCA) fails to learn the true low-dimensional subspace in such cases. This is the reason why robust versions of PCA, which put a penalty on arbitrarily large outlying entries, are preferred to perform dimension reduction. In this paper, we argue that it is necessary to study the presence of outliers not only in the observed data matrix but also in the orthogonal complement subspace of the authentic principal subspace. In fact, the latter can seriously skew the estimation of the principal components. A reinforced robustification of principal component pursuit is designed in order to cater to the problem of finding out both types of outliers and eliminate their influence on the final subspace estimation. Simulation results under different design situations clearly show the superiority of our proposed method as compared with other popular implementations of robust PCA. This paper also showcases possible applications of our method in critically tough scenarios of face recognition and video background subtraction. Along with approximating a usable low-dimensional subspace from real-world data sets, the technique can capture semantically meaningful outliers.

Series elastic actuators (SEAs) have become fundamental components in robots that physically interact with the surrounding world and with humans. Force control of SEAs is indeed an active area of research. This paper refines and improves the stability analysis of the environment-adaptive force controller we previously proposed and proves asymptotic convergence in several cases of practical interest. In particular, we theoretically motivate observations that are not adequately explained or covered in our previous work. The analysis reveals an interesting generalization property of the approach achieved by using a simple and generic model to account for very different environments including stiff contacts, purely inertial loads, and soft materials. This is allowed by model parameters with interchangeable physical meaning: the same parameter describes different physical properties or variables, depending on the kind of interacting environment. In this light, the proposed adaptive controller adapts not only to parameters values, but also to parameter meaning. The analysis also shows that as the environment stiffness decreases, the convergence precision may degrade and a sliding-mode robustification is proposed to overcome the issue. Simulations and experiments are conducted to validate the control convergence in different environments, showing agreement with the theoretical expectation.

The authors propose automated algorithmic error resilience based on outlier detection. The approach exploits characteristic behavior of a class of applications to create metric functions that normally produce values according to a designed distribution or behavior and produce outlier values when computations are affected by errors. For a robust algorithm that employs such an approach, error detection becomes equivalent to outlier detection. As such, the authors use well-established, statistically rigorous techniques for outlier detection to effectively and efficiently detect errors, and subsequently correct them. The authors' error-resilient algorithms incur significantly lower overhead than traditional hardware and software error-resilience techniques (such as triple modular redundancy). In addition, compared to previous approaches to application-based error resilience, the authors' approaches parameterize the robustification process, making it easy to automatically transform large classes of applications into robust applications with the use of parser-based tools and minimal programmer effort. They demonstrate the use of automated error resilience based on outlier detection for dynamic programming problems. For error rates up to 10E-3, the error-resilient algorithms achieve the same output quality as their error-free counterparts with significantly lower overhead (less than 59 percent for monadic problems and 263 percent for polyadic problems, on average) than conventional hardware and software error-resilience techniques.

An adaptive approach is proposed to address the problem of robustification (and performance loss reduction) of anti-windup (AW) compensation in the presence of large uncertainties. The approach relies on the augmentation of the AW compensator with an adaptive robustifying filter; a constructive procedure is provided for designing such a filter. The use of adaptation allows to reduce the conservativeness of previous robust approaches to what is needed to preserve stability with respect to the uncertainty actually present on the plant during operation, as detected by input and output plant measurement.

In this paper, we design feedback controllers for lateral and transversal axes of an atomic force microscope (AFM) piezoelectric tube scanner. The controllers are constrained to keep the standard deviation of the measurement noise fed back to the displacement output around 0.13 nm. It is shown that the incorporation of appropriate inner loops provides disturbance rejection capabilities and robustness against dc gain uncertainties, two requirements for satisfactory operation of signal transformation method. Simulations and experiments show significant improvement of steady-state tracking error with signal transformation, while limiting the projected measurement noise.

This paper presents a methodology for enhancing the robustness of a generalized predictive control (GPC) controlled system by convex optimization of the Youla parameter. This methodology requires, as a first step, the design of an initial GPC controller; this controller is then robustified considering frequency and time-domain constraints. By means of the Youla parametrization, frequency and time-domain constraints are formulated within a convex optimization framework, then the optimal parameter is deduced solving this optimization problem. The developed robustified GPC controller is finally applied on a benchmark including an induction motor, aiming at reducing the impact of measurement noise and inertia variation of the system, while respecting a time-domain template for the disturbance rejection. Comparison with results obtained with a more "classical" proportional-integral-derivative (PID) controller is finally given.

This note deals with controller robustification using equivalent observer based structure. The purpose of the method is to improve the parametric robustness of an initial controller, synthesized, for example, by using the LQ,H/sub /spl infin//, or /spl mu/ technique. The method uses equivalent Luenberger observer formulation and multimodel design procedure for the parametric robustification. First, the initial controller is put under equivalent observer structure. Second, from this observer-based formulation, the controller is reshaped to increase the parametric robustness without paying attention to the closed-loop poles coming from the controller dynamics. Note that during this step, changes in the controller initial performances are minimized. Another advantage of the equivalent observer based formulation lies with the fact that it could directly be used to schedule the controller (dynamic and feedback parts). Finally, the global method (equivalent observer plus robustification) is applied on the robust control of the space shuttle described in /spl mu/-analysis and synthesis toolbox.

This letter presents a robustification of the preconditioned Filtered-X LMS algorithm proposed by Elliott et al.. The method optimizes the average performance for probabilistic uncertainty in the secondary path and relaxes the SPR condition for global convergence. It also prevents large amplification in the pre-conditioning filters due to secondary path zeros on and/or close to the unit circle, which may yield overactuation in practical applications.

This paper presents for the first time, the design of an adaptive controller for the control of the ram velocity of an injection moulding machine, based on a physical nonlinear model which describes the filling phase in detail. Polynomial series are first used to simplify the mathematical model such that the system parameters and the system states are essentially separated. The errors arising from the approximation are reduced with the introduction of certain robustification terms into the control law representative of the residual effects. A sliding surface is subsequently defined and a self-tuning version of robust control capable of achieving tight set-point regulation is proposed in which the control gains are tuned by an adaptive algorithm. The convergence property of the adaptive controller is proven in the paper. Finally, simulations are provided to evaluate the performance of the proposed control system.

We propose a new feedback controller architecture. The distinguished feature of our new controller architecture is that it shows structurally how the controller design for performance and robustness may be done separately which has the potential to overcome the conflict between performance and robustness in the traditional feedback framework. The controller architecture includes two parts: one part for performance and the other part for robustness. The controller architecture works in such a way that the feedback control system can be solely controlled by the performance controller when there is no model uncertainties and external disturbances and the robustification controller can only be active when there are model uncertainties or external disturbances.

We present two redesigns to robustify backstepping control laws against dynamic uncertainties at the input of the plant. In the first redesign we construct a passivating control law from a control Lyapunov function obtained by backstepping. In the second redesign we simplify the passivation redesign which becomes cumbersome for high order systems. We also review the two main versions of backstepping: cancellation and L/sub G/V-backstepping, and compare their stability margins.

This paper presents a constructive robust adaptive nonlinear control scheme which can be regarded as a robustification of the now popular adaptive backstepping algorithm. The allowed class of uncertainties includes nonlinearly appearing parametric uncertainty, uncertain nonlinearities, and unmeasured input-to-state stable dynamics. The adaptive control laws proposed in this paper do not require any dynamic dominating signal to guarantee the robustness property of Lagrange stability. The numerical example of a simple pendulum with unknown parameters and without velocity measurement illustrates our theoretical results.

This paper describes a fast and robust method for identifying the leverage points of a linearized power system state estimation model. These are measurements whose projections on the space spanned by the row vectors of the weighted Jacobian matrix, the so-called factor space, do not follow the pattern of the bulk of the point cloud. In other words, their projections are outliers in the factor space. The proposed method is implemented through a new version of the projection algorithm that accounts for the sparsity of the Jacobian matrix. It assigns to each data point a projection statistic defined as the maximum of the standardized projections of the point cloud on some directions passing through the origin. By comparing these projection statistics to cutoff values, one can identify the outliers in the factor space and thereby pinpoint the leverage points. The projection statistics are also used to derive weights for robustifying the weighted least absolute value estimator. The computational efficiency and the robustness of the method are demonstrated on the IEEE-14 bus and 118-bus systems.

A robustification procedure for LQ state feedback design is presented. Such a procedure consists of choosing the state and input weighting matrices according to the kind of uncertainties on the system. Both structured and norm-bounded additive uncertainties are addressed, and upper bounds for the uncertainties that do not destabilize the closed-loop system are presented. Connections with the quadratic stabilizability problem are established. >

A sufficient condition for quadratic stabilizability of uncertain linear discrete-time systems using LQ regulators is presented. The true system is represented by a nominal model plus additive terms representing the most common types of uncertainties in the state and input matrices. Sufficient robustness bounds as well as a robustification procedure for discrete-time LQ regulators are presented and the conservativeness of the proposed condition is discussed. >

The paper addresses the problem of determining which one of a finite number of nonlinear dynamic systems generated a given noisy measured signal. An approximate likelihood ratio test (LRT) is proposed that consists of bank of an extended Kalman filters (EKFs) each tuned to one of the candidate signal models. The prediction error sequences of each EKF are used to form the LRT since each is nominally approximately zero mean Gaussian with known covariance if it matches the measured signal. The Gaussian approximation is good at high signal-to-noise ratios (SNRs) and when the signal models are close to linear, but can degrade rapidly as the SNR decreases, or when the system becomes increasingly nonlinear. The paper proposes a robustification of the test that is based on a generalization of Huber's (1965) robust LRTs. Simulations are used to examine the performance of the proposed tests. >

Robust estimation is studied of two-dimensional (2-D) power spectra of signals which are adequately represented by Gaussian random field models but for which there are imperfect observations. Two situations of particular interest occur when the contaminating noise is additive and when the contaminating noise appears in the innovations. In these cases, the observed data are not Gaussian and conventional procedures are no longer efficient. To estimate the parameters of the signal model from the contaminated data, two procedures are described which were originally proposed for estimation of scale and location from independent data and adapted to one-dimensional autoregression parameter estimation by previous researchers. The first algorithm is a robustification of least squares and equivalent to an iterated weighted least-squares problem where the weights are data-dependent. The second algorithm is an iterative procedure known as a filter cleaner. Experiments using the robust procedures with synthetic data are reported and the results compared to a conventional method of model-based spectrum estimation. >

The capacity of the arbitrarily varying channels with states sequence known to the sender is determined. The result is obtained with the help of an elimination technique and a robustification technique. It demonstrates once more the power of these techniques.