Parameter Identification Of Systems Research Articles

A ranking improved teaching-learning-based optimization algorithm for parameters identification of photovoltaic models

Solar energy is an important clean energy source, primarily applied for photovoltaic (PV) power generation. The precise identification of PV system parameters is critical for system control and simulation, posing a challenge due to the models' non-linearity, implicitness, and multiple optimal properties. So, a ranking improved teaching-learning-based optimization (RITLBO) is developed in this work to solve the problem of identifying the parameters of the PV model. RITLBO is a meta-heuristic algorithm based on teaching-learning-based optimization (TLBO) that simulates classroom teacher-student interaction. In RITLBO, learners are classified into inferior and superior groups based on their fitness ranking. During the teacher phase, superior learners emulate the top three agents with the highest fitness for local search, while inferior learners engage in guided mutual learning for global search, effectively utilizing computing resources. In the learner phase, superior learners receive guided information, while inferior learners engage in broader information exchange, balancing exploration and exploitation. RITLBO and fourteen algorithms are used to identify the parameters for five different PV models to confirm that the RITLBO is effective. Statistical results and analysis demonstrate that RITLBO is accurate and reliable in identifying PV model parameters. RITLBO offers promising prospects in optimizing PV system parameters through its unique strategies.

System identification for structural condition assessment: Application to critical neoclassical monuments in Nepal

This paper presents structural condition assessment of some critical neoclassical monuments in Nepal using nonparametric and parametric system identification techniques. Neoclassical buildings in Nepal house critical administrative units. Structural condition assessment of such buildings is therefore important for the safety of their occupants and interrupted function during and after major earthquakes. The historical and cultural values of such monuments necessitate regular structural assessment for maintenance, repair, and preservation. Dynamic characteristics such as natural vibration frequencies of structures provide valuable insights on their structural condition. While numerical models are commonly used to estimate eigen frequencies of structures, they are associated with large uncertainties in massive monuments with complex structural and geometrical configurations. Noninvasive dynamic identification techniques provide an alternative means in such structures. Ambient vibration records from six neoclassical monuments in the Kathmandu Valley, Nepal are used in this study to estimate vibration frequencies of the structures in different states. The structures were damaged during the 2015 Gorkha earthquake, and subsequently retrofitted. Changes in vibration frequencies before and after retrofitting provide useful insights on structural improvement through retrofitting. Input-output and output-only system identification techniques are tested to simplify the structural condition assessment approach. We conclude that the state space system identification can stably quantify dynamic properties so stiffness variation can be confidently extracted, which is the key application for noninvasive structural system identification. Also, using minimum number of sensors, we captured damage aggravation deploying the modal assurance criterion. The outcomes indicate that structural condition assessment of complex structures is possible using a limited number of sensors for circumstances such as damage aggravation to temporal variation (evolution/reduction) of dynamic characteristics.

Backbone curve orientated parameter identification for systems with coupled nonlinearity

Backbone curve attracts much concern from nonlinear system identification studies because it directly orientates the nonlinearity embedded in a system. Although extensive approaches have been proposed for normal nonlinearities that are symmetric or decoupled in the modal space, it is still challenging to extend the backbone-based identification technique for better applicability. To this end, this paper considers a series of representative 2-degree-of-freedom systems with asymmetric and coupled nonlinearities. First, the expression of the backbone curve, derived by the method of multiple scales (MMS), is augmented from only the driving-point distribution to the transfer-point distribution because of the coupling. Then, a hierarchical decoupling scheme is constructed to decompose the coupling-induced multi-modes in the transfer-point responses, making the backbone extraction technique valid for systems with coupled nonlinearities. Finally, the system parameters are identified by fitting the extended backbone functions to the decomposed samples. Numerical and experimental examples are both provided to demonstrate the detailed operations of the identification algorithm. The results show that by introducing the augmented backbone curves, the system parameters can be identified completely, indicating a successful improvement to the existing backbone-based identification technique.

Compressive stochastic subspace identification: A modal parameter identification method suitable for compressive sampling

Stochastic subspace identification (SSI) and its variants are widely used in conventional structural health monitoring (SHM) owing to their high computation robustness and estimation accuracy. Conventional SHM is based on a Nyquist (high-rate and uniform) sampling dictated by Shannon–Nyquist sampling theorem. As data quantity of SHM is increasing with duration and equipment at an ever-growing rate and new applications of SHM with strict constricts on sampling are emerging one by one, sub-Nyquist sampling, also known as compressive sampling has increasingly been used in SHM in recent years. Therefore, despite the success of SSI method and its algorithmic variants, a significant drawback lies in their handling of sub-Nyquist data. To overcome this drawback, we set out to extend the applicable scenarios of SSI from Nyquist (high-rate/uniform) sampling to sub-Nyquist (low-rate/nonuniform) sampling. In this paper, we propose a compressive stochastic subspace identification (CSSI) which can identify modal parameters from sub-Nyquist/compressed data. Specifically, CSSI involves two aspects: covariance matrix reconstruction and system parameter identification. We find the complete covariance matrix in classic SSI can also be estimated/reconstructed by sub-Nyquist samples. More importantly, this reconstruction can be physically guaranteed through special sampling patterns without sparsity prior. Based on the above findings, we develop a compressed covariance sensing-based covariance matrix reconstruction technique and derive the optimal sub-Nyquist sampling pattern such that the reconstruction is hassle-free and efficient. Then, we perform modal parameter identification on the basis of the reconstructed covariance matrix. Finally, the effectiveness of CSSI is validated by simulations and experiments. In short, CSSI, as a new extension of SSI, can extract modal parameters at lower sampling rates than what is prescribed by the classic SSI. The implication of this is the possibility that CSSI for operational modal analysis will require fewer measurements thus it would reduce the storage requirement and the bandwidth for transmitting data.

Piezoelectric Hinged Beam with Arc Mass Stopper for Vibration and Human Motion Energy Harvesting.

Compact reliable structure and strong electromechanical coupling are hot pursuits in piezoelectric vibration energy harvester (PVEH) design. PVEH with a static arc stopper makes piezoelectric stress uniformly distributed and widens the frequency band by collision but wastes space. This Article proposes a hinged PVEH with two arc mass stoppers (AS-H-PVEH). Two arc stoppers as movable masses increase the vibration energy and the effective electromechanical coupling coefficient to achieve strong electromechanical coupling. AS-H-PVEH generates a 4.1 mW power output at 11.6-12.0 Hz and 0.2 g. AS-H-PVEH sustains 4 g acceleration vibration for 10 min without attenuation. To offset the resonance frequency increase caused by arc contact, we discuss the magnetic coupling, and axial force effects are discussed. The design of the arc stopper radius, nonlinear electromechanical coupling model, and system parameter identification method are presented. The displacement varied mechanical quality factor and effective electromechanical coupling coefficient are considered in the modified model for the first time. The model obtained good agreement under experiments. The power generation and driven wireless sensor performance of AS-H-PVEH was verified. This research has important theoretical and application value for the performance optimization of PVEH with an arc stopper.

Parameter identification of linear systems with heat sensors subject to unknown space-varying parameters

We extend existing works on control of (finite-dimensional) linear systems with (infinite-dimensional) heat sensors [6]. The extension lies on the one hand, in the fact that all system poles and zeros are unknown and, on the other, in the fact that sensor equations include unknown spatially-varying parameters. These uncertainties make the problem of parameter identification quite challenging. The existing adaptive observers for this type of systems prove not to be applicable in the presence of these uncertainties. Presently, we develop an identification method that involves multi-sine exciting input signals. Doing so, the inaccessible signal at the junction point, between the system and the sensor, can be given an affine parameterized form. It turns out that the identification problem of the sensor is decoupled from that of the system. Then, a parameter estimator involving K-filters is designed to get estimates of the sensor parameters as well as those of the inaccessible signal at the junction point. A new persistent excitation concept is introduced that ensures exponential convergence of the sensor parameter estimates and the estimated junction signal. The latter is then used to identify the parameters of the system dynamics.

Parameter identification of dynamical systems based on short-term prediction by the generalized cell mapping method with deep learning

Parameter identification of dynamical systems based on short-term prediction by the generalized cell mapping method with deep learning

Parameter identification of fractional-order systems with time delays based on a hybrid of orthonormal Bernoulli polynomials and block pulse functions

Parameter identification of fractional-order systems with time delays based on a hybrid of orthonormal Bernoulli polynomials and block pulse functions

SVD-Based Parameter Identification of Discrete-Time Stochastic Systems with Unknown Exogenous Inputs

This paper addresses the problem of parameter identification for discrete-time stochastic systems with unknown exogenous inputs. These systems form an important class of dynamic stochastic system models used to describe objects and processes under a high level of a priori uncertainty, when it is not possible to make any assumptions about the evolution of the unknown input signal or its statistical properties. The main purpose of this paper is to construct a new SVD-based modification of the existing Gillijns and De Moor filtering algorithm for linear discrete-time stochastic systems with unknown exogenous inputs. Using the theoretical results obtained, we demonstrate how this modified algorithm can be applied to solve the problem of parameter identification. The results of our numerical experiments conducted in MATLAB confirm the effectiveness of the SVD-based parameter identification method that was developed, under conditions of unknown exogenous inputs, compared to maximum likelihood parameter identification when exogenous inputs are known.

Highly-efficient filtered hierarchical identification algorithms for multiple-input multiple-output systems with colored noises

Multiple-input multiple-output (MIMO) systems have extensive applications in industrial processes and systems engineering. This letter applies the filtering identification idea to establish a filtered identification model and investigate a filtered auxiliary model-based recursive least squares (F-AM-RLS) algorithm for parameter identification of MIMO systems with colored noises. To improve the computational efficiency, this work further proposes a four-stage filtered auxiliary model-based recursive least squares (4S-F-AM-RLS) algorithm by means of the hierarchical identification principle. Then, by incorporating the forgetting factor, a four-stage filtered auxiliary model-based forgetting factor recursive least squares (4S-F-AM-FF-RLS) algorithm is given to improve the convergence speed and the estimation accuracy. Additionally, the computational complexity analysis of the proposed algorithms indicates that the 4S-F-AM-RLS algorithm effectively reduces the computational burden and improves computational efficiency. Finally, the effectiveness of the F-AM-RLS, 4S-F-AM-RLS and 4S-F-AM-FF-RLS algorithms is validated through a numerical example.

Simultaneous Identification of Multiple Parameters in Wireless Power Transfer Systems Using Primary Variable Capacitors

This paper proposes a novel approach to simultaneously identifying multiple critical parameters in a wireless power transfer (WPT) system, such as the resonant frequency, mutual inductance, and load resistance, solely from the primary side. The key is to adopt a primary-side-switch-controlled capacitor (SCC) to ensure that the imaginary part of the input impedance is only caused by the secondary-side reflected impedance at three predesigned frequencies. The DSP controller then samples and processes the primary voltage and current using a gradient descent algorithm to derive the above parameters. After the identification, the SCC adjusts its equivalent capacitance based on the secondary-side practical resonant frequency to ensure a zero-phase angle (ZPA), thereby significantly improving the compatibility of the WPT system with unknown receivers. Compared to the previous frequency-sweeping method, the proposed approach is simpler and more suitable for deployment on the controller. Finally, experimental results demonstrate that the identification error of mutual inductance and resonant frequency are within 7.5% and 2.68%, respectively.

Application of the evolutionary approach to structural and parametric identification of dynamic objects

The paper examines the evolutionary approach to structural and parametric identification of dynamic systems in the form of differential equations. The approach is based on a genetic programming algorithm to determine a structure of the equation and differential evolution method for parameters selection. The author proposed approach based on such evolutionary algorithms as genetic programming and differential evolution. The search for the structure is carried out by genetic programming. The selection of numerical parameters and initial conditions is implemented by a method of differential evolution. The problem of finding a model that describes changes in the efficiency of a hydraulic system is solved with the help of this approach. The proposed approach is compared with a recurrent neural network and a nonparametric kernel regression estimation.

Parameter Identification of Discrete-time Linear Time-invariant Systems Using State and Input Data

Parameter Identification of Discrete-time Linear Time-invariant Systems Using State and Input Data

Sampling in Parametric and Nonparametric System Identification: Aliasing, Input Conditions, and Consistency

Sampling in Parametric and Nonparametric System Identification: Aliasing, Input Conditions, and Consistency

System identification based structural damage aggravation detection in a large masonry building

System identification is widely used to estimate the dynamic characteristics of structures using either nonparametric or parametric approaches. Parametric system identification constructs mathematical models to estimate dynamic characteristics such as natural frequency, damping ratio, and mode shapes. Also, the same approach can be used to characterize damage if periodic measurements are available. In this paper, we report damage aggravation in a large neoclassical masonry structure using system identification. We took measurements in 2019 and 2023 in the Institute of Engineering, Dean Office building, which was damaged by the 2015 Gorkha, Nepal earthquake. The building is a neoclassical three storied construction with a central courtyard and an extension along the N-S direction. Without major interventions and strengthening, the building is still in use. Using the numerical algorithms for subspace state space system identification (N4SID), vibration frequencies of the structure are identified for both measurements. Also, mode shapes are identified, and damage aggravation is detected using natural frequency and MAC parameters. The first mode frequency along the N-S direction is reduced by 44% and that along the E-W direction is reduced by 1.15%, which indicates a major damage aggravation along the N-S (Y) direction.

Parameter identification of the linear discrete-time stochastic systems with unknown exogenous inputs

The paper addresses a parameter identification problem for linear discrete-time stochastic systems with unknown exogenous inputs. Such systems are considered when solving practical problems related to the measurements processing in the case when it is impossible to do any assumptions about the evolution of unknown input signal or its statistical characteristics that can change over time. We consider a class of discrete time linear stochastic systems with unknown exogenous inputs, where an additional source of a priori uncertainty of the system model is introduced, namely, the unknown parameter, on the elements of which the system model matrices can depend. This formulation of the parameter identification problem under the conditions of unknown inputs and the presence of random noises describes a high degree of uncertainty of a discrete time linear stochastic system. We propose a novel solution to this problem based on the construction of a new instrumental identification criterion. Minimization of this criterion allows for evaluating the unknown system model parameters simultaneously with the estimating of the state vector and unknown exogenous inputs of the system. Numerical experiments confirm the validity and efficiency of the proposed parameter identification method.

Adaptive Impact Mitigation Based on Predictive Control with Equivalent Mass Identification

The paper presents the concept of equivalent parameter predictive control (EPPC) elaborated for semi-active fluid-based (hydraulic and pneumatic) shock absorbers equipped with controllable valves and subjected to impact excitation. The undertaken problem concerns the absorption and dissipation of the impact energy with the requirement to minimize the generated reaction force and corresponding impacting object deceleration. The development of a control strategy for a challenging problem with unknown impacting object mass and unknown changes of external and disturbance forces is proposed and discussed in detail. The innovative solution utilizes the paradigm of model predictive control supplemented by the novel concept of equivalent system parameters identification. The EPPC is based on the online measurement of system response, the computation of the equivalent mass of the impacting object, and the repetitive solution of the optimal control problem with various prediction intervals and constraints imposed on valve opening. The presented method is proven to operate robustly for unknown excitations, including double-impact conditions, and it has similar efficiency to control methods developed previously for known impact parameters.

Application of an Output Filtering Method for an Unstable Wheel-Driven Pendulum System Parameter Identification

This research aims to apply an output filtering method to conduct the system parameter identification of an unstable wheel-driven pendulum system. First, the nonlinear dynamic model of the system is established by utilizing the Lagrangian dynamic theorem. Next, the Least-Square (LS) is introduced for system parameter identification formulation. Nevertheless, considering the real scenario, the wheel displacement is acquired from encoders subject to quantization errors. The pitch angle of the pendulum cart is also accompanied by Gaussian noise. Therefore, using numerical differentiation for angular acceleration in the LS estimations directly would induce incorrect state information seriously. To address this practical issue, an output filtering method is considered. The developed parameter identification algorithm could attenuate the influence of the quantization effect as well as noisy data and thus obtain much more accurate parameter identification results. Comparative simulation reveals that the output filtering method has a superior parameter estimation performance than the direct numerical difference method.

Comparison of Constrained Unscented and Cubature Kalman Filters for Nonlinear System Parameter Identification

Comparison of Constrained Unscented and Cubature Kalman Filters for Nonlinear System Parameter Identification

Parameters identification of the satellite attitude control system with large inertia rotating components

This paper investigates the unbalance parameter identification of the large inertia rotating component of satellite. Firstly, the dynamics model with unbalance parameter of the large inertia rotating component is established. Then, based on the principle of parameter separation and decoupling, a modified two-stage exogenous Kalman filter (TSXKF) algorithm is proposed. This method works directly on nonlinear system, estimates the centroid position, the centroid velocity, attitude angular, attitude angular velocity, and identifies the nonlinear unknown static unbalance parameter, which is the centroid offset. Finally, the simulation results verify the effectiveness of the method.