Modal analysis of an operational offshore wind turbine using enhanced Kalman filter-based subspace identification

Operational modal analysis (OMA) is an essential tool for understanding the structural dynamics of offshore wind turbines (OWTs). However, the classical OMA algorithms require the excitation of the structure to be stationary white noise, which is often not the case for operational OWTs due to the presence of periodic excitation caused by...

Closed-loop Aspects of Data-Enabled Predictive Control

In recent years, the amount of data available from systems has drastically increased, motivating the use of direct data-driven control techniques that avoid the need of parametric modeling. The aim of this paper is to analyze closed-loop aspects of these approaches in the presence of noise. To analyze this, a unified formulation of several...

Tensor networks for MIMO LPV system identification

In this paper, we present a novel multiple input multiple output (MIMO) linear parameter varying (LPV) state-space refinement system identification algorithm that uses tensor networks. Its novelty mainly lies in representing the LPV sub-Markov parameters, data and state-revealing matrix condensely and in exact manner using specific tensor...

Nonparametric identification of time-varying human joint admittance

In this study, a nonparametric method, developed in Lataire et al. (2012), is applied to the identification of linear time-varying human joint admittance. The aim of the method, denoted Skirt Decomposition method, is to reconstruct the time-varying system function. The main contribution of the paper is to evaluate the possibilities and...

Data-driven repetitive control: Wind tunnel experiments under turbulent conditions

A commonly applied method to reduce the cost of wind energy, is alleviating the periodic loads on turbine blades using Individual Pitch Control (IPC). In this paper, a data-driven IPC methodology called Subspace Predictive Repetitive Control (SPRC) is employed. The effectiveness of SPRC will be demonstrated on a scaled 2-bladed wind turbine....

Revealing time-varying joint impedance with kernel-based regression and nonparametric decomposition

During movements, humans continuously regulate their joint impedance to minimize control effort and optimize performance. Joint impedance describes the relationship between a joint's position and torque acting around the joint. Joint impedance varies with joint angle and muscle activation and differs from trial-to-trial due to inherent...

Identification of 2D Interconnected Systems: An efficient Steepest-Descent approach

In this paper, a new identification method for large 2D grids of interconnected systems is presented. The proposed algorithm minimizes the Output-Error of the lifted system by using a Steepest-Descent optimization method which exploit the Multilevel Sequentially-Semi Separable (MSSS) structure of the involved matrices. Furthermore, it is...

Recursive nuclear norm based subspace identification

Nuclear norm based subspace identification methods have recently gained importance due to their ability to find low rank solutions while maintaining accuracy through convex optimization. However, their heavy computational burden typically precludes the use in an online, recursive manner, such as may be required for adaptive control. This...

Predictor-Based Tensor Regression (PBTR) for LPV subspace identification

The major bottleneck in state-of-the-art Linear Parameter Varying (LPV) subspace methods is the curse-of-dimensionality during the first regression step. In this paper, the origin of the curse-of-dimensionality is pinpointed and subsequently a novel method is proposed which does not suffer from this bottleneck. The problem is related to the...

Frequency-domain optimization of fixed-structure controllers

This paper aims to introduce a new approach to optimize the tunable controller parameters of linear parameterizable controllers. The presented approach is frequency-domain based and can therefore directly be used to tune, among others, proportional integral derivative controllers, low/high-pass filters, and notch filters, using a Frequency...