in this paper, stability analysis of walking gaits and robustness analysis are developed for a five-link and four-actuator biped robot. stability conditions are derived by studying unactuated dynamics and using the poincarã© map associated with periodic walking gaits. a stable gait is designed by an optimization process satisfying physical constraints and stability conditions. also, considering...

Robustness and efficiency of the residual scale estimators in the regression model is important for robust inference. We introduce the class of robust generalized M-scale estimators for the regression model, derive their influence function and gross-error sensitivity, and study their maxbias behavior. In particular, we find overall minimax bias estimates for the general class and also for well-...

ANOVA tests are the standard tests to compare nested linearmodels fitted by least squares. These tests are equivalent to likelihood ratio tests, so they have high power. However, least squares estimators are very vulnerable to outliers in the data, and thus the related ANOVA type tests are also extremely sensitive to outliers. Therefore, robust estimators can be considered to obtain a robust al...

A test for symmetry of the distribution of the errors in a linear model is proposed. It is a goodness-of-fit type test based on the discrepancy between two robust fits. The first fit is appropriate under symmetric errors while the second is appropriate for skewed as well as symmetric distributions. The proposed test is robust and is asymptotically distribution free. Besides deriving the test st...

Variable speed wind turbines maximize the energy capture by operating the turbine at the peak of the power coefficient, however parametric uncertainties and disturbances may limit the efficiency of a variable speed turbine. In this study, we present a robust backstepping approach for the variable speed control of wind turbines. Specifically, to overcome the undesirable effects of parametric unc...

Optimal robust M-estimates of a multidimensional parameter are described using Hampel’s infinitesimal approach. The optimal estimates are derived by minimizing a measure of efficiency under the model, subject to a bounded measure of infinitesimal robustness. To this purpose we define measures of efficiency and infinitesimal sensitivity based on the Hellinger distance. We show that these two mea...

This paper deals with several challenging problems of robust filtering for two-dimensional (2-D) systems. First of all, new linear matrix inequality (LMI) characterizations for the and 2 norms of 2-D systems are introduced and thoroughly established. Based on these preparatory results, convex (LMI) characterizations for robust , 2, and robust mixed 2 filtering are derived. The efficiency and vi...

Robust model predictive control (MPC) has been investigated widely in the literature. However, for industrial applications, current robust MPC methods are too complex to employ. In this paper, a discrete-time recurrent neural network model is presented to solve the minimax optimization problem involved in robust MPC. The neural network has global exponential convergence property and can be easi...

In this paper, we introduce a new class of robust autocorrelation estimators based on interpreting the sample autocorrelation function as a linear regression. We investigate the efficiency and robustness properties of the estimators that result from employing three common robust regression techniques. Construction of robust autocovariance and positive definite autocorrelation estimates is discu...