Dynamic Identification Method Research Articles

A Dynamic Early Warning Model for Flash Floods Based on Rainfall Pattern Identification

AbstractFlash floods are one of the most devastating natural hazards in mountainous and hilly areas. In this study, a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning. A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database. The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data. Subsequently, the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. To verify the proposed model, three small watersheds in China were selected as case studies. The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall. With the increase of measured rainfall information, the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency (NSE) exceeding 0.8 at t = 4, t = 5, and t = 6, thereby improving the accuracy of flash flood warnings. Simultaneously, the proposed model extends the forecast lead time with high accuracy. For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed, the proposed model issues early warnings two hours and three hours before the end of the rainfall, respectively, with a warning accuracy of more than 0.90. The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.

Low-resource dynamic loading identification of nonlinear system using pretraining

To address the challenge of load identification in nonlinear systems, an audio neural network-based method called WaveNet is proposed that leverages its capability to capture long-term dependencies in mechanical systems, enabling accurate load identification. Unlike traditional dynamic load identification methods that often encounter difficulties with matrix solutions, this approach takes advantage of WaveNet’s capabilities, enhancing both accuracy and efficiency. We integrate pre-training and transfer learning techniques to address the data scarcity challenges often encountered in real-world engineering applications. By transferring features across distributed datasets, this method reduces the dependency on single-task data, thereby improving model robustness. The performance of the WaveNet method is rigorously evaluated against traditional benchmarks such as Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) benchmarks under random load conditions applied to a complex structural framework. The proposed method achieves a root mean squared error (RMSE) of 1.521 and a determination coefficient (R²) of 0.996 in the random load case, demonstrating superior accuracy compared to other approaches. Moreover, its applicability is verified through simulations of both impact load and harmonic load scenarios, showcasing the effectiveness of transfer learning in overcoming domain discrepancies. Finally, the method is tested in random experiments to validate its engineering applicability. The results highlight the significant accuracy improvement in low-resource tasks achieved through pre-training, showcasing the potential and value of the proposed method.

Dynamic parameter identification method for wireless charging system of AUV based on multi-strategy nonlinear rime algorithm

Dynamic parameter identification method for wireless charging system of AUV based on multi-strategy nonlinear rime algorithm

Research on improved dynamic load identification method based on Kalman filter under noise interference

As a mathematical approach for solving inverse problems, the dynamic load identification method is particularly useful when directly measuring the load acting on a structure is challenging. The Kalman filter (KF) algorithm is commonly employed for dynamic load identification. However, in practice, measurement noise can significantly affect the accuracy of the KF algorithm's results. In this study, we investigate the impact of response noise on the accuracy of load identification results using the KF algorithm. To eliminate the influence of measurement noise on identification accuracy, we propose an improved dynamic load identification method. This method incorporates adaptive techniques to assess varying levels of response noise and improve the load identification accuracy. Notably, this method is applicable in both physical and modal domains. We conduct numerical simulations using a 5-DOF system under different noise conditions. Compared to traditional dynamic load identification method based on KF algorithm, the improved method consistently achieves superior accuracy in load identification across various noise levels. Moreover, an experiment is conducted to further validate the feasibility of the proposed method. The results showed that the proposed method is effective and can achieve high-precision identification of unknown loads.

A method for dynamic parameter identification of an industrial robot based on frequency response function

AbstractHaving accurate values of the dynamic parameters is necessary to characterize the dynamic behaviors of mechanical systems and for the prediction of their responses. To accurately describe the dynamic characteristics of industrial robots (IRs), a new method for dynamic parameter identification is proposed in this study with the goal of developing a real IR dynamics model that combines the multibody system transfer matrix method (MSTMM) and the nondominated sorting genetic algorithm‐II (NSGA‐II). First, the multibody dynamics model of an IR is developed using the MSTMM, by which its frequency response function (FRF) is calculated numerically. Then, the experimental modal analysis is conducted to measure the IR's actual FRF. Finally, the objective function of the minimum errors between the calculated and measured eigenfrequencies and FRFs are constructed to identify the dynamic parameters of the IR by the NSGA‐II algorithm. The simulated and experimental results illustrate the effectiveness of the methodology presented in this paper, which provides an alternative to the identification of IR dynamic parameters.

A dynamic identification method for general serial manipulators from an analytical perspective based on Lie theory

A dynamic identification method for general serial manipulators from an analytical perspective based on Lie theory

Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair. With its lightweight design, high load-bearing capacity, and smooth surface, the coupled-drive joint is particularly well suited for these robots. However, the coupled nature of the joint disrupts the direct linear relationship between the input and output torques, posing challenges for dynamic modeling and practical applications. This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics. Building on this foundation, the Newton-Euler method was used to develop a dynamic model for the entire robotic arm. A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero. An experimental method was designed to compensate for gravity, inertia, and modeling errors to identify the parameters of the friction model. This method establishes a mapping relationship between the friction force and motor current. In addition, a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm. Trajectory tracking experiments were conducted during the experimental validation phase, demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm. This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms, thereby establishing a foundation for motion control in humanoid nursing robots.

Dynamic Identification Method of Load Adjustable Capacity Based on Load Dynamic Characteristic Generation

Abstract With the continuous establishment of the energy internet, there are already regions carrying out demand response pilots by installing smart meters. In the case that complete communication has not yet been established, how to mine the potential value of collected smart meter data based on the existing pilot areas to obtain the real response potential of regional temperature control equipment at each moment is a common focus of attention in both academia and industry. In this paper, we propose a method for dynamically identifying load adjustable capacity based on generated load dynamic characteristics, which enhances the load feature library and dynamically identifies load adjustable capacity based on generated load dynamic characteristics. The method only installs a smart energy measuring device at the customer’s entrance line to obtain the customer’s total load power information and dynamically recognizes the adjustable capacity of the load based on the generation of load dynamic characteristics.

Static and dynamic characteristics of supersonic cruise missile with damaged wing

AbstractAccurately evaluating the aerodynamic performance of the missile with damaged structures is very important for the subsequent flight control strategy. At present, few researchers have studied the aerodynamic characteristics of damaged supersonic cruise missiles. Based on CFD (computational fluid dynamics) solutions and the dynamic derivative identification method, the differences in static and dynamic characteristics between the damaged and undamaged models are compared. The results indicate that when the extent of damage increases, the change rate of drag coefficient at larger AoA (angle-of-attack) is greater than that at the smaller AoA. On the contrary, the change rate of lift coefficient at larger AoA is smaller than that at smaller AoA. Meanwhile, the absolute value of the static pitch moment decreases, but the absolute value of the roll moment increases. Damage causes a change in the absolute values of the pitch and roll dynamic derivatives, and the dynamic derivatives do not vary monotonically with the increase of AoA. The turning point occurs at about $\alpha$ = 5°. The areas of the hysteresis loops of the pitch-roll coupling moment increase, which makes the dynamic coupling characteristic between the pitch and roll directions increase. Finally, the maximum allowable damage extent of the missile wing that can achieve static trim is obtained and validated by controlling the deflection of the four rudders.

Dynamic Load Identification at Natural Frequencies for Aircraft via Attention Based 1D-CNN

Since the frequency response function is ill-conditioned at the natural frequency of the system, the traditional load identification method based on the system parameters is no longer applicable. Aiming at the difficulty of dynamic load identification at the natural frequency of the structural system, a dynamic load identification method at the natural frequency of the structure based on one-dimensional convolutional neural network(1D-CNN) with attention mechanism is proposed. Specifically, the high-level features in the vibration response signal are first extracted through the convolution layer. Then the weight matrix of the network is updated by backpropagation algorithm, which represents the importance of different features. The mapping relationship between response and load is established to realize the task of load identification. From the trained data, the attention module learns the contribution of features according to the different contribution of different features to load prediction. The important components in the response signal are highlighted and noise pollution is suppressed. Excitation and response signals at the natural frequency of the system were acquired using exciters and an accelerometer mounted on the GARTEUR aircraft model. Excitation and response signals at the natural frequencies of the system are obtained by an exciter and accelerometer mounted on the GARTEUR aircraft model. The responses of the model at the first three natural frequencies of 6.4Hz,35.8Hz and 48.5Hz were obtained respectively. Experimental results show that compared with the traditional TSVD load identification method, the maximum error of this method is only 3.19%. Compared with the 1D-CNN method, the proposed method has stronger robustness under 20%, 50% and 80% noise levels.

Dynamic identification methods and artificial intelligence algorithms for damage detection of masonry infills

The failure of non-structural components after an earthquake is among the most expensive earthquake-incurred damage, and may also have life-threatening consequences, especially in public buildings with very crowded facilities, because exposition is high and the risk increases accordingly. The assessment of existing non-structural components is particularly complex because in-depth in situ investigation is necessary to detect the presence of deficiencies or damage. This problem concerns interior and exterior partitions made of various materials (e.g., glass and masonry), as well as equipment and facilities in construction (building, industry, and infrastructure). Defining the boundary conditions of these components is of paramount importance. Indeed, external restraints (i) affect dynamic properties and, thus, the action experienced during an earthquake, and (ii) influence the capacity to detach the component before failure from the bearing structure (e.g., an infill wall connected to the main structural frame, or equipment connected to secondary structural members such as floors). The authors, therefore, conducted environmental vibration tests of an infill wall and refined a finite element model to simulate typical damage scenarios to be implemented on the wall. Selected damage scenarios were then artificially realized on the existing infill and further ambient vibration tests were performed to measure the accelerations for each of them. Finally, the authors used these accelerations to detect the damage by means of established OMA, as well as innovative machine learning techniques. The results showed that convolutional variational autoencoders (CVAE), coupled with a one-class support vector machine (OC-SVM), identified the anomaly even when the OMA exhibited limited effectiveness. Moreover, the machine learning procedure minimizes human interaction during the damage detection process.

A novel dynamic parameter identification method for the multi-degrees-of-freedom series direct drive manipulator

Accurate dynamic parameters are essential for precise control of model-based manipulators. Due to the direct connection between the joint axis and the motor rotor of the direct drive manipulator, using additional joints would increase the disturbance of the system, leading to substantial trajectory tracking errors, which could potentially harm the manipulator if the tracking error exceeds the limited position of joints, and it also curtails the comprehensive parameter excitation and affects the identification accuracy. Restricting joint movement to a narrow area can prevent this injury, but it also curtails the comprehensive parameter excitation and affects the identification accuracy. To address these challenges, this paper presents a novel method for identifying dynamic parameters of multiple degree-of-freedom (DOF) series direct drive manipulators. Specifically, an open-loop gravity torque compensation strategy with spatial geometric characteristics is introduced to ensure secure operation during manipulator identification processes. Meanwhile, an improved model reference adaptive identification (MRAI) method is presented to estimate the nominal inertia quickly and accurately. Finally, a disturbance observer (DOB) based on minimum model perturbation bound is proposed to compensate for significant variations in model disturbances, enabling stable and precise tracking of planned exciting trajectories. This proves advantageous for fully exciting dynamic parameters and improving identification accuracy. Simulation experiments demonstrate that the tracking error of each joint’s exciting trajectory is within 0.07 radians, and the maximum absolute error of identifying dynamic parameters is 0.099169409, which verifies the feasibility and effectiveness of the proposed method.

A dynamic graph structure identification method of spatio-temporal correlation in an aluminum electrolysis cell

The dynamic correlation analysis of cell-spatial information (distributed anode current signal, DACS) is of great significance in the regional-refined control of industrial aluminum electrolysis cell. Due to the strong-dynamic spatio-temporal correlation of DACS and the complex dynamic cell noise, the existing methods are difficult to effectively obtain the spatio-temporal correlation analysis results of aluminum electrolysis cell. To solve these problems, a dynamic graph structure identification method of spatio-temporal correlation (DGSI-StC) in an aluminum electrolysis cell is proposed. The identified dynamic graph structure is used to describe the dynamic correlation analysis results of cell-spatial information. Specifically, a novel strongly robust distance function Edit Distance on Real sequences with Adaptive threshold (At-EDR) is proposed to weaken the impact of complex dynamic cell noise on the dynamic correlation analysis of cell-spatial information. Under the guidance of aluminum electrolysis process mechanism knowledge, the matrix-representation of dynamic cell noise information obtained from production data is used as the adaptive threshold of At-EDR to construct the dynamic graph structure. Then, based on the single-layer classical graph convolutional network, a framework for optimizing the dynamic graph structure is designed to capture the strong-dynamic spatio-temporal correlation of DACS. The framework obtains the optimal dynamic graph structure by optimizing the hyper-parameters. Finally, the experimental results on the public datasets show that the robustness of At-EDR is superior to existing methods. Meanwhile the experimental results on multiple sets of industrial aluminum electrolysis production datasets show that DGSI-StC improves the classification accuracy by 2.82% compared with existing classification methods.

Research on the Simulation Method for Equivalent Stiffness of Bolted Connection Thin Plate Structures

Bolted connections are widely used in assembly structures, and their dynamic characteristics are often affected by stiffness, damping, excitation, and other factors. In order to solve the problems of low computational efficiency of fine modeling and large computational error of linearized equivalent modeling of bolted structures, this paper proposes a dynamic characteristic parameter identification method for bolted structures based on the multiscale method and considering the influence of nonlinear factors. In this method, the bolted connection characteristics are simulated in the form of a combination of shear stiffness, torsional stiffness, nonlinear stiffness, and viscous damping coefficient and identified according to the test measurement frequency and frequency response function. At the same time, by establishing the nonlinear dynamic model of bolted structure, the influence of different bolt preloads and excitation forces on the dynamic characteristics of bolted structure is studied.

A Non-Global Traversal Method for Dynamic Load Rapid Localization and Identification

Dynamic load localization and identification technology is very important in the structural design and optimization of aircraft. This paper proposes a non-global traversal method (NTM) for the fast positioning and recognition of dynamic loads on continuous beams. This method separates the load’s position and amplitude information in the modal space. Then, it constructs an interpolation function about position information, and converts load positioning to solving the zero point of the interpolation function. After determining the position of the dynamic load, the amplitude of the dynamic load is recognized. This method does not need to traverse all the position points globally, thereby greatly improving the efficiency of load positioning. Numerical simulations and experiments show that compared with the original variable separation fast positioning method (VSRPM), this method improves the calculation efficiency by more than 80% while maintaining the same recognition accuracy. NTM is a new method of great application value.

Inverse WKB recursive solution method for dynamic load identification of linear time-varying structural systems

Inverse WKB recursive solution method for dynamic load identification of linear time-varying structural systems

The Real-Time Dynamic Prediction of Optimal Taxi Cruising Area Based on Deep Learning

A real-time, effective, and dynamic taxi cruising recommendation strategy is essential to solving the problem of taxi cruising passenger difficulty and urban road traffic congestion. This study focuses on two aspects of the real-time accessible range and pick-up ratio (PR) and proposes a real-time dynamic identification method for taxi optimal cruise-seeking area. Firstly, based on the cumulative opportunity method, a univariate temporal convolutional network (UTCN) accessible range dynamic prediction model is proposed to predict the real-time accessible range of taxis. Secondly, based on the gradient boosting decision tree (GBDT) model, the influencing factors with a high correlation with the PR are selected from the four dimensions of traffic characteristics, environmental meteorology, and time and space variables. Then, a multivariate univariate temporal convolutional network (MTCN) global grid PR prediction model is constructed, and the optimal taxi cruising area is identified based on the maximum PR. The results show that the taxi accessible range and PR of the same grid in different periods change with time, and based on the model comparison, the accessible range and PR prediction results of UTCN and MTCN algorithms in different periods are the best to identify the optimal cruising area of taxis in different periods. The main contribution of this study is that the proposed optimal cruising area prediction model has timeliness, accessibility, and dynamics. It can not only improve the probability of taxis receiving passengers and avoid taxis cruising aimlessly, but also solve the shortage of taxis in hotspots, thus shortening the waiting time of passengers. This provides a scientific basis for improving taxi cruising efficiency and the government’s formulation of taxi operation management policies, which can effectively promote the sustainable development of urban traffic.

A dynamic parameter identification method for the 5-DOF hybrid robot based on sensitivity analysis

Purpose This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification. Design/methodology/approach First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified. Findings Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models. Originality/value This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

Dynamic Adaptation RFF Identification Method Leveraging Cognitive Representation Learning

Dynamic Adaptation RFF Identification Method Leveraging Cognitive Representation Learning

An improved dynamic model identification method for small unmanned helicopter

PurposeThe purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.Design/methodology/approachThis method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.FindingsThe algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.Originality/valueThis paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.