Lateral directional aircraft aerodynamic parameter estimation using adaptive stochastic nonlinear filter

This paper aims to accurately estimate the lateral directional aerodynamic parameters in real time irrespective of the variations in the process and measurement covariance matrices. The proposed algorithm for parameter estimation is based on the integration of adaptive techniques into a stochastic nonlinear filter. The proposed adaptive estimation algorithm is applied to flight test data, and the lateral directional derivatives are estimated in real time. The estimates are compared with those obtained from the Filter Error Method (FEM), an offline parameter estimation method accounting for process noise. The estimation results are observed to be very comparable, and the supremacy of the adaptive filter is illustrated by varying the covariance matrices of both process and measurement noises. The parameters estimated by the adaptive filter are found to converge to their actual values, whereas the estimates of the regular filter are observed to diverge from the actual values when changing the noise covariance matrices. The proposed adaptive algorithm can estimate the lateral directional aerodynamic derivatives more accurately without prior knowledge of either process or measurement noise covariance matrices. Hence, it is of great value in online implementations.

Inverse optimal adaptive output feedback control of a class of Euler–Lagrange systems: A nonlinear filter based approach

In this paper, we present an inverse optimal tracking controller for a class of Euler–-Lagrange systems having uncertainties in their dynamical terms under the restriction that only the output state ( i.e. position for robotic systems) is available for measurement. Specifically, a nonlinear filter is used to generate a velocity substitute, then a controller formulation ensuring a globally asymptotically stable closed-loop system while minimizing a performance index despite the presence of parametric uncertainty, is proposed. The stability proof is established using a Lyapunov analysis of the system with proposed optimal output feedback controller. Inverse optimality is derived via designing a meaningful cost function utilizing the control Lyapunov function. Numerical simulations are presented to illustrate the viability and performance of the derived controller.

A feature extraction method of ship-radiated noise based on mathematical morphological filtering

In order to extract feature of ship signal more effectively, we propose a new approach for mathematical morphological filtering based on the morphological features. Mathematical morphological filter is a new nonlinear filter, which can effectively extract the edge contour and shape characteristics. The stimulation signal is processed by mathematical morphological filtering of different structure elements, which confirms the effect of morphological filtering on suppressing noise and preserving the nonlinear characteristics. Using flat structure element, the measured ship-radiated noise signals are processed by average filter, and the filtered signals are analyzed on the frequency spectrum. Compared with other filters, the result shows that the mathematical morphological filtering can successfully extract the effective information from the ship-radiated noise signals.