Battery management systems (BMS) must estimate the state‐of‐charge (SOC) of the battery accu‐ rately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and ar‐ tificial neura...

This paper describes the application of Lyapunov stability theory to automatic steering of ships. The motivation for using nonlinear control theory is that nonlinearities can be compensated for in a systematic manner. For instance, this allows the designer to design the autopilot system for a ship described by the nonlinear models of Norrbin (1963) and Bech and Wagner Smith (1969) both utilizin...

The goal of this work is to study the asymptotic and finite sample properties of an estimator of a nonlinear regression function when errors are spatially correlated, and when the spatial dependence structure is unknown. The proposed method is based on a weighted nonlinear least squares approach, taking into account the spatial covariance. Weak consistency of the regression parameters estimator...

In this Brief Report we discuss the effectiveness of the Kozachenko-Leonenko entropy estimator when generalized to cope with entropic forms customarily applied to study systems evincing asymptotic scale invariance and dependence (either of linear or nonlinear kind). We show that when the variables are independently and identically distributed the estimator is only valuable along the whole domai...

In this paper, the performance of different estimators in estimating the speech signal through Quantum parameters can be analyzed. The main objective is to estimate the speech signal by a set of linear and Non-linear estimators that are proposed to be efficient in performance. The Minimax mean square error estimator is designed to minimize the worst-case MSE. In an estimation context, the objec...

This paper proposes a moving horizon estimator for nonlinear systems with unknown inputs, which do not comply with the model structures proposed in the literature for the design of nonlinear observers. The estimator is designed as an optimization problem over a moving horizon, constrained to process model equations and considering the unknown inputs as random inputs among their operating bounds...

This paper considers least squares estimators for regression problems over convex, uniformly bounded, uniformly Lipschitz function classes minimizing the empirical risk over max-affine functions (the maximum of finitely many affine functions). Based on new results on nonlinear nonparametric regression and on the approximation accuracy of maxaffine functions, these estimators are proved to achie...

Nonparametric estimation methods for the multivariable frequency response function are experimentally evaluated using closed-loop data from an industrial robot. Three classical estimators (H1, joint input-output, arithmetic mean) and two estimators based on nonlinear averaging techniques (harmonic mean, geometric/logarithmic mean) are considered. The estimators based on nonlinear averaging give...

This paper treats stabilization of multiphase flow in a gas lifted oil well. Two different controllers are investigated, PI control using the estimated downhole pressure in the well, and nonlinear model based control of the total mass in the system. Both control structures rely on the use of a state estimator, and are able to stabilize the well flow with or without a downhole pressure measureme...