G: The CAPTAIN Toolbox

Extended State Dependent Parameter modelling with a Data-Based Mechanistic approach to nonlinear model structure identification

Identification of non-stationary continuous systems using modulating functions

A method is presented that makes practicable the identification of nonstationary Wiener systems. The method employs a single-record estimator for the evaluation of the time-variant system kernels, thus relaxing the strict requirement of the ergodic hypothesis that has so far limited applications of the Wiener theory to stationary systems. The estimator is unbiased and consistent under fairly weak conditions for the system function, thus opening a host of new possibilities in the analysis of nonstationary phenomena.

Identification of dynamical systems is, in essence, a problem of observability of system parameters from a related Bet of observations. In this article, a necessary and sufficient condition is obtained for the observability of parameters from a given record of inputs and states of a linear system with a class of parameter variations. Also obtained is n necessary and sufficient condition of observability of a Sub-class of such systems with polynomial system and constant output coefficient matrices when only the outputs instead of the states are accessible for measurement.

This paper presents high automated control solutions implemented on a unique software platform designed for real-time data acquisition, system identification and control. In the same time a comparison study between pole placement method and LQG optimal control for a MIMO industrial process will be presented. A special module for data process, developed for robust Matlab algorithms integration, is included and described on the following chapters.

<strong class="journal-contentHeaderColor">Abstract.</strong> Multi-hazard risk assessments for building portfolios exposed to earthquake shaking followed by a tsunami are usually based on empirical vulnerability models calibrated on post-event surveys of damaged buildings. The applicability of these models cannot easily be extrapolated to other regions of larger/smaller events. Moreover, the quantitative evaluation of the damages related to each of the hazards type (disaggregation) is impossible. To investigate cumulative damage on extended building portfolios, this study proposes an alternative and modular method to probabilistically integrate sets of single-hazard vulnerability models that are being constantly developed and calibrated by experts from various research fields to be used within a multi-risk context. This method is based on the proposal of state-dependent fragility functions for the triggered hazard to account for the pre-existing damage, and the harmonisation of building classes and damage states through their taxonomic characterization, which is transversal to any hazard-dependent vulnerability. This modular assemblage also allows us to separate the economic losses expected for each scenario on building portfolios subjected to cascading hazards. We demonstrate its application by assessing the economic losses expected for the residential building stock of Lima, Peru, a megacity commonly exposed to consecutive earthquake and tsunami scenarios. We show the importance of accounting for damage accumulation on extended building portfolios while observing a dependency between the earthquake magnitude and the losses derived for each hazard scenario. For the commonly exposed residential building stock of Lima exposed to both perils, we find that classical tsunami empirical fragility functions lead to an underestimation of predicted losses for lower magnitudes (M_w) and large overestimations for larger Mw events in comparison to our state-dependent models and cumulative damage method.

A new adaptive algorithm for blind identification of time-varying MA channels is derived. This algorithm proposes the use of a novel system of equations derived by combining the third- and fourth-order statistics of the output signals of MA models. This overdetermined system of equations has the important property that it can be solved adaptively because of their symmetries via an overdetermined recursive instrumental variable-type algorithm. This algorithm shows good behaviour in arbitrary noisy environments and good performance in tracking time-varying systems.

A novel crosscorrelation technique of obtaining dynamic measurements on the systems controlling a 2-axis stable platform carrying a large antenna is reported. Rotation of the antenna causes platform control parameters to be periodically time varying, the periodicity being comparable with control-system time constants. A theoretical analysis of the technique is reported that shows that measurements are realistic, uncorrupted and suitable for determining the nature of compensating device such as a digital filter. Results from measurements on the system, together with results from offline and online simulations, are presented.

The problem of identification of a nonstationary stochastic system is considered, and an estimation method based on the functional series modeling (FSM) of the system parameter trajectory is proposed for its solution. It is shown that the parameter-matching properties of FSM estimators can be described in terms of the appropriately defined (time-varying) impulse and frequency responses. It is suggested and verified by means of computer simulation that the averaged frequency characteristics associated with FSM estimators can yield useful information about their parameter-matching abilities. >

We suggest a method for reconstructing the coefficients of a linear nonstationary system from its responses to generalized controls (delta functions and their generalized derivatives).

In this paper the airspeed control in an open circuit wind tunnel for a velocity range from 5 m/s to 25 m/s is presented. The instrument used for the velocity measurement was a type L Pitot tube, differential pressure, absolute pressure and temperature transducers were used too. Open loop tests were developed in the wind tunnel to determine modeling it through the Toolbox for system identification of MATLAB® for the design of the reference speed control strategy. By the process of identification the PI controller that is capable of maintaining a variation of ±2% with respect to the set point was obtained.

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.

in this paper we studied the performance of several distributed adaptive algorithms for non-stationary sparse system identification. Non-stationarity is a feature that is introduced to adaptive networks recently and makes the performance of them degraded. The performance analyses are carried out with the steady-state mean square deviation (MSD) criterion of adaptive algorithms. Some sparsity aware algorithms are considered in this paper which tested in non-stationary systems for the first time. It is presented and proved that the performance of incremental least means square/forth (ILMS/F) algorithm surpasses all other algorithms as non-stationarity grows. We hope that this work will inspire researchers to look for other advanced algorithms against systems that are both non-stationary and sparse.

In this work, a novel design of a portable leg rehabilitation system (PLRS) is presented. The main purpose of this paper is to provide a portable system, which allows patients with lower-limb disabilities to perform leg and foot rehabilitation exercises anywhere without any embarrassment compared to other devices that lack the portability feature. The model of the system is identified by inverse kinematics and dynamics analysis. In kinematics analysis, the pattern of motion of both leg and foot holders for different modes of operation has been investigated. The system is modeled by applying Lagrangian dynamics approach. The mathematical model derived considers calf and foot masses and moment of inertias as important parameters. Therefore, a gait analysis study is conducted to calculate the required parameters to simulate the model. Proportional derivative (PD) controller and proportional-integral-derivative (PID) controller are applied to the model and compared. The PID controller optimized by hybrid spiral-dynamics bacteria-chemotaxis (HSDBC) algorithm provides the best response with a reasonable settling time and minimum overshot. The robustness of the HSDBC–PID controller is tested by applying disturbance force with various amplitudes. A setup is built for the system experimental validation where the system mathematical model is compare with the estimated model using system identification (SI) toolbox. A significant difference is observed between both models when applying the obtained HSDBC–PID controller for the mathematical model. The results of this experiment are used to update the controller parameters of the HSDBC-optimized PID.

A unified derivation for fast estimation algorithms by the conjugate direction method