Modal parameter identification by adaptive parameter domain with multiple genetic algorithms

Guan Xiaoying,Xie Shengjia,Chen Guo,Qu Meijiao 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.12

The identification of aero-engine dynamic parameters is fundamental to establishing accurate dynamic models, which has a great effect on the accuracy of model calculation. The accurate parameter range, however, is not easy to define in practical engineering applications. In many cases, it could only be constructed out of experience. In order to reduce the impact of initial parameter interval accuracy on the identification results, the adaptive parameter domain with multiple genetic algorithms is proposed to identify the aeroengine dynamic model parameters, through which the vibration modal parameter identification is studied under the condition of initial uncertainty of the parameters. The effectiveness of the adaptive parameter domain method is verified through the third-order model with severe modal coupling, which also proves the efficiency and rapidity of finding the correct value of parameters and, indicates that it would not be necessary to introduce an exact definition of the initial interval of parameters. All these suggest that the proposed multiple genetic algorithms of the adaptive parameter domain has a good reference value for engineering applications.

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

Yusheng Wei 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.1

Parameter identification involves two fundamental problems, the problem of identifiability and the problem of designing identification algorithms without using knowledge about system parameters. It is well-known that certain initial conditions can destroy identifiability. To avoid problematic initial conditions, we propose the concept of identifiability regardless of the initial condition for deterministic discrete-time linear time-invariant systems. Analysis shows that such an identifiability notion is equivalent to controllability. Identification of controllable systems with state and input measurements is achieved by proposing an algebraic approach. We observe from system dynamics that system parameters are the solution to a set of linear equations. The solution is unique if a data matrix constructed by snapshots of system state and input is invertible. Under a one-step delayed linear state and input feedback law, the data matrix is invertible if and only if the initial input does not belong to a finite set. A one-step delayed input feedback law incurs input sequences that violate a well-known persistent excitation condition for parameter identification. The motivation to initiate an input that escapes the finite set facilitates the design of an iterative algorithm to identify system parameters in finite time. We have shown that parameter identification can be still achieved without satisfying a persistent excitation condition regarding input sequence design.

ASimple Online Identification Method Using Sliding Mode Technique

Shinichiro Nakagawa,EikoFurutani 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

In this paper, a simple online identification method for single-input single-output systems utilizing the idea of sliding mode control is proposed. The method uses a simplified model with an adjustable parameter of the plant, adjusts the parameter so that the error between outputs of the plant and the model for a step input convergesto zero, and gives an identification result as the adjusted parameter. Through a single identification procedure one parameter of the plant can be obtained. The simulation result shows that the method is useful for parameter identification of plants.

Parameter Identification of Induction Motors using Variable-weighted Cost Function of Genetic Algorithms

Ahmed Chaouki Megherbi,Hassina Megherbi,Khier Benmahammed,Abdel Ghani Aissaoui,Ahmed Tahour 대한전기학회 2010 Journal of Electrical Engineering & Technology Vol.5 No.4

This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.

Machine learning‑based parameter identification method for wireless power transfer systems

Hao Zhang,Ping-an Tan,Xu Shangguan,Xulian Zhang,Huadong Liu 전력전자학회 2022 JOURNAL OF POWER ELECTRONICS Vol.22 No.9

Parameter identification is an effective way to obtain uncertain parameters of wireless power transfer (WPT) systems, which is essential to achieving robust control and efficiency improvement. The traditional method relies on the phase lock of the primary impedance angle or lengthy algorithm iterations, and the identification depends on a high sampling accuracy and is time-consuming. In this study, a flexible parameter identification method based on the fusion of a machine learning model and a circuit model is proposed. Taking the primary voltage and current as input characteristic factors, support vector regression (SVR) is used to establish a machine learning model for coupling coefficient identification. In addition, the optimal model parameters are sought based on the grid search method. On the basis of coupling coefficient identification, the circuit model is used to realize the identification of the load resistance. Finally, the effectiveness of the proposed parameter identification method for a WPT system is verified by experimental results.

Time-varying modal parameters identification of large flexible spacecraft using a recursive algorithm

Zhiyu Ni,Zhigang Wu,Shunan Wu 한국항공우주학회 2016 International Journal of Aeronautical and Space Sc Vol.17 No.2

In existing identification methods for on-orbit spacecraft, such as eigensystem realization algorithm (ERA) and subspace method identification (SMI), singular value decomposition (SVD) is used frequently to estimate the modal parameters. However, these identification methods are often used to process the linear time-invariant system, and there is a lower computation efficiency using the SVD when the system order of spacecraft is high. In this study, to improve the computational efficiency in identifying time-varying modal parameters of large spacecraft, a faster recursive algorithm called fast approximated power iteration (FAPI) is employed. This approach avoids the SVD and can be provided as an alternative spacecraft identification method, and the latest modal parameters obtained can be applied for updating the controller parameters timely (e.g. the self-adaptive control problem). In numerical simulations, two large flexible spacecraft models, the Engineering Test Satellite-VIII (ETS-VIII) and Soil Moisture Active/Passive (SMAP) satellite, are established. The identification results show that this recursive algorithm can obtain the time-varying modal parameters, and the computation time is reduced significantly.

An Integrated Parameter Identification Method of Asynchronous Motor Combined with Adaptive Load Characteristics

Kang Zhong-Jian,Sun Yi-Sen,Liu Jia-Xuan 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.2

The existing asynchronous motor parameter identification methods only identify the parameters of the asynchronous motor itself, ignoring the identification of the parameters of the load carried by the asynchronous motor. This paper proposes an integrated parameter identification method of the asynchronous motor that uses the improved PSO (Particle Swarm Optimization, PSO) and considers the load adaptive characteristics. Compared with the traditional method, this method firstly combines the PSO method with Space Disturbance (SD) to form an improved PSO method, which prevents the PSO from falling into a local optimal state and enhances the global optimization ability of the PSO method. Secondly, according to the characteristics of different loads, a load identification strategy is constructed. This strategy can judge the type of load carried by the asynchronous motor, which reduces the optimization exploration space of the PSO algorithm and accelerates the optimization speed of the PSO algorithm. Finally, according to the identified load types, the improved particle swarm optimization algorithm combined with the spatial disturbance is used to realize the integrated identification of the asynchronous motor and the load parameters. The validity of the algorithm is verified by an example, and the factors affecting the identification accuracy are analyzed.

Identification of isotropic and orthotropic constitutive parameters by FEA-free energy-based inverse characterization method

Shang, Shen,Yun, Gun Jin,Kunchum, Shilpa,Carletta, Joan Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.4

In this paper, identification of isotropic and orthotropic linear elastic material constitutive parameters has been demonstrated by a FEA-free energy-based inverse analysis method. An important feature of the proposed method is that it requires no finite element (FE) simulation of the tested material. Full-field displacements calculated using digital image correlation (DIC) are used to compute DIC stress fields enforcing the equilibrium condition and DIC strain fields using interpolation functions. Boundary tractions and displacements are implicitly recast into an objective function that measures the energy residual of external work and internal elastic strain energy. The energy conservation principle states that the residual should be zero, and so minimizing this objective function inversely identifies the constitutive parameters. Synthetic data from simulated testing of isotropic materials and orthotropic composite materials under 2D plane stress conditions are used for verification of the proposed method. When identifying the constitutive parameters, it is beneficial to apply loadings in multiple directions, and in ways that create non-uniform stress distributions. The sensitivity of the parameter identification method to noise in both the measured full-field DIC displacements and loadings has been investigated.

Parameter identification for dual‑phase shift modulated DAB converters

Tan‑Quoc Duong,Sung‑Jin Choi 전력전자학회 2021 JOURNAL OF POWER ELECTRONICS Vol.21 No.12

Deadbeat control is an effective method for controlling the output voltage of dual active bridge converters. However, its effectiveness depends on the model parameter accuracy. In practice, the model parameters of dual active bridge converters vary depending on the operation conditions, manufacturing tolerances, and calendar aging. This leads to performance degradation and causes steady-state errors of the output voltage. To overcome the effect of parameter mismatch, this study proposed an algorithm to achieve the online identification of two model parameters, i.e., the series inductor and the output capacitor. Based on a least-squares analysis, the online parameter identification of a dual active bridge converter under dualphase shift modulation is implemented to obtain the actual values of model parameters. Consequently, the steady-state errors of the output voltage are immediately mitigated after every sampling period when the optimal predicted phase shift duty ratios are updated. The proposed algorithm was tested through both simulations and experiments to verify its effectiveness.

등가정하중법을 이용한 비선형 동적 시스템의 파라미터 식별 및 모델 간소화

정민호(Min-Ho Jeong),량티에이(Tie-Yi Liang),양현익(Hyun-Ik Yang),박경진(Gyung-Jin Park) 한국자동차공학회 2022 한국 자동차공학회논문집 Vol.30 No.12

A parameter identification method of nonlinear dynamic systems is proposed. The error function of the finite element model is defined by the given input/output data and is minimized through structural optimization. In general, nonlinear dynamic response optimization is costly. This study uses the equivalent static load method to perform nonlinear dynamic response optimization for the identification. After identification is completed, the error function is reduced since the simulation value is close to the experimental value. Eventually, the finite element model becomes more accurate. The proposed method is applied to three numerical examples. The results indicate that the proposed method can minimize the model error through parameter identification.