A Comparison of Methods for Online Lever Arm Estimation in GPS/INS Integration

A comparison of neural network, state augmentation, and multiple model-based approaches to online location of inertial sensors on a vehicle is presented that exploits dualantenna carrier-phase-differential GNSS. The best technique among these is shown to yield a significant improvement on a priori calibration with a short window of data. Estimation of Inertial Measurement Unit (IMU) parameters is a mature field, with state augmentation being a strong favorite for practical implementation, to the potential detriment of other approaches. A simple modification of the standard state augmentation technique for determining IMU location is presented that determines which model of an enumerated set best fits the measurements of this IMU. A neural network is also trained on batches of IMU and GNSS data to identify the lever arm of the IMU. A comparison of these techniques is performed and it is demonstrated on simulated data that state augmentation outperforms these other methods.Aerospace Engineering and Engineering Mechanic

Adaptive weighted least squares algorithm for Volterra signal modeling

Published versio

Design of a deep neural network-based integral sliding mode control for nonlinear systems under fully unknown dynamics

In this letter a novel deep neural network based integral sliding mode (DNN-ISM) control is proposed for controlling perturbed systems with fully unknown dynamics. In particular, two DNNs with an arbitrary number of hidden layers are exploited to estimate the unknown drift term and the control effectiveness matrix of the system, which are instrumental to design the ISM controller. The DNNs weights are adjusted according to adaptation laws derived directly from Lyapunov stability analysis, and the proposal is satisfactorily assessed in simulation relying on benchmark examples

An intelligent parameter varying (IPV) approach for non-linear system identification of base excited structures

Health monitoring and damage detection strategies for base-excited structures typically rely on accurate models of the system dynamics. Restoring forces in these structures can exhibit highly non-linear characteristics, thus accurate non-linear system identification is critical. Parametric system identification approaches are commonly used, but require a priori knowledge of restoring force characteristics. Non-parametric approaches do not require this a priori information, but they typically lack direct associations between the model and the system dynamics, providing limited utility for health monitoring and damage detection. In this paper a novel system identification approach, the intelligent parameter varying (IPV) method, is used to identify constitutive non-linearities in structures subject to seismic excitations. IPV overcomes the limitations of traditional parametric and non-parametric approaches, while preserving the unique benefits of each. It uses embedded radial basis function networks to estimate the constitutive characteristics of inelastic and hysteretic restoring forces in a multi-degree-of-freedom structure. Simulation results are compared to those of a traditional parametric approach, the prediction error method. These results demonstrate the effectiveness of IPV in identifying highly non-linear restoring forces, without a priori information, while preserving a direct association with the structural dynamics

Real-time system identification using deep learning for linear processes with application to unmanned aerial vehicles

This paper proposes a novel parametric identification approach for linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT). The proposed methodology utilizes MRFT to reveal distinguishing frequencies about an unknown process; which are then passed to a trained DL model to identify the underlying process parameters. The presented approach guarantees stability and performance in the identification and control phases respectively, and requires few seconds of observation data to infer the dynamic system parameters. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented methodology. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.Comment: 13 pages, 9 figures. Submitted to IEEE access. A supplementary video for the work presented in this paper can be accessed at: https://www.youtube.com/watch?v=dz3WTFU7W7c. This version includes minor style edits for appendix and reference

Parameter Identification of Micro-Grid Control System

Micro-grid provides an effective means of integrating distributed energy resource (DER) units into the power systems. A micro-grid is defined as an independent low- or medium-voltage distribution network comprising various DER units, power-electronic interfaces, controllable loads, and monitoring and protection devices. Following the development of the renewable energy, micro-grid has attracted much attention. This thesis emphasizes on the parameter identification of the control system of the micro-grid. The control system plays an important role in the stable operation of the micro-grid. The micro-grid has two operation modes, which are grid-connected operation mode and islanded operation mode. The transition between two operation modes of the micro-grid often occurs according to the condition of the entire grid. In order to make this process smooth, the control system is crucial, and the parameters of the control system is critical to the disturbance suppression during the process of transition. In the thesis, a method combining least square method with Newton-Raphson algorithm is proposed. In order to prove the utility of the method, the parameter identification of a typical control system and its several separated elements are simulated in MATLAB. This method can identify multiple parameters at the same time and have fast convergence