Dynamic Parameter Identification Research Articles

Research on dynamic parameter identification method of shallow reservoir based on kalman filter

Research on dynamic parameter identification method of shallow reservoir based on kalman filter

Technology of cybernetic models’ creating for the synthesis and refinement of autonomous underwater vehicle motion control system controllers

The technology of cybernetic models’ creating for the synthesis and refinement of algorithms for controlling the autonomous underwater vehicle’s (AUV’s) motion is considered. The paper gives a description of the following sequence of actions performed at each stage of the cybernetic model development: dynamic model type and parameters identification, controller type selection, construction and analysis of the considered control loop block diagram, determination of the controller’s optimal parameters. The synthesis of heading, pitch and depth stabilization regulators for streamlined shape AUV during propulsion engines controlling is performed. The results of numerical modeling and research of the various factors influence on the quality of control are presented.

Human-robot coupling dynamic modeling and analysis for upper limb rehabilitation robots.

Upper limb rehabilitation robots have become an important piece of equipment in stroke rehabilitation. Human-robot coupling (HRC) dynamics play a key role in the control of rehabilitation robots to improve human-robot interaction. This study aims to study the methods of modeling and analysis of HRC dynamics to realize more accurate dynamic control of upper limb rehabilitation robots. By the analysis of force interaction between the human arm and the upper limb rehabilitation robot, the HRC torque is achieved by summing up the robot torque and the human arm torque. The HRC torque and robot torque of a 2-DOF upper limb rehabilitation robot (FLEXO-Arm) are solved by Lagrangian equation and step-by-step dynamic parameters identification method. The root mean square (RMS) is used to evaluate the accuracy of the HRC torque and the robot torque calculated by the parameter identification, and the error of both is about 10%. Moreover, the HRC torque and the robot torque are compared with the actual torque measured by torque sensors. The error of the robot torque is more than twice the HRC. Therefore, the HRC torque is more accurate than the actual torque. The proposed HRC dynamics effectively achieves more accurate dynamic control of upper limb rehabilitation robots.

A Lie-Theory-Based Dynamic Parameter Identification Methodology for Serial Manipulators

Accurate estimation of the dynamic parameters comprising a robot's dynamics model is of paramount importance for simulation and real-time model-based control. The conventional approaches for obtaining the identification model are extremely cumbersome, and incapable of offering universal applicability, as well as physical feasibility of dynamic parameter identification. To this end, the work presented herein proposes a novel and generic identification methodology, for retrieving the dynamic parameters of serial manipulators with arbitrary degrees of freedom (DOFs), based on the Lie theory. In this approach, the robot dynamics model that includes frictional terms is analytically represented as a closed-form matrix equation, by rearranging the classical recursive Newton–Euler formulation. The link inertia matrix that comprises inertia tensors, masses, and Center of Mass (CoM) positions, together with the joint friction coefficients, are extracted from the regrouped linear dynamics model by means of the Kronecker product. Meanwhile, the introduced Kronecker–Sylvester identification equation is formulated as an optimization problem involving dynamic parameters with physical feasibility constraints, and is ultimately estimated via linear matrix inequality techniques and semidefinite programming using joint position, velocity, acceleration, and torque data. Identification results of dynamic parameters are accurately procured through a series of practical tests that entail providing a seven-DOF Rokae xMate robot, with optimized Fourier-series-based excitation trajectories. Experimental validation serves the purpose of demonstrating the proposed method's efficacy, in terms of accurately retrieving a serial manipulator's dynamic parameters.

Modal parameter identification by adaptive parameter domain with multiple genetic algorithms

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.

Online state-of-health estimation of lithium-ion battery based on dynamic parameter identification at multi timescale and support vector regression

Accurate real-time State-of-Health (SoH) estimation is important in ensuring battery safety and promoting efficiency. The open circuit voltage (OCV), relative to State-of-Charge (SoC), changes as a battery degrades. In this paper, we propose using an extended Kalman filter-recursive least squares parameter identification method based on the second-order RC equivalent circuit model. SoC estimation, RC model parameter identification and OCV-SoC relationship revision can be conducted simultaneously at three timescales to improve the identification accuracy. Then, by analyzing the relationship between battery SoH and the model parameters, we establish a support vector regression algorithm for online SoH estimation. Finally, the dynamic stress test and the federal urban driving schedule test are adopted and used as practical simulations to verify the feasibility of the method under dynamic conditions. The results show that the proposed method achieves highly accurate estimations and has strong robustness under both static and dynamic conditions.

Structural Dynamic Load and Parameter Identification Based on Dummy Measurements of Displacement

Structural load and parameter identification are the essential contents in the field of structural dynamics, and some studies pay attention to the coupled recognition of uncertain structure parameters as well as unacquainted loads. Gillijns and De Moor presented the Kalman-type filter, which was used in the work for coupled identification as the unabbreviated form of GDF. However, it has been demonstrated that it is unstable for the extended GDF (EGDF) method, drifting in the identified unacquainted loads as well as displacement, just like most previous identification methods based on the least-square algorithm. In order to deal with this unstable issue, this paper applied the dummy measurements of displacements on a position level and modifies the EGDF algorithm using the information integration method about the accelerated measurements with dummy measurements. Numerical example of a truss is used for validating the applicability of the method in the work, and an influence of covariance matrices in dummy displacements is also considered.

Dynamic identification of a tracking parallel mechanism

To improve tracking precision against complex external forces in the working environment, dynamic model-based control algorithms are widely used in tracking mechanisms. As a foundation of the real-time dynamic control system, this paper focuses on the complete predicted dynamic modelling and dynamic parameter identification of a 2-DoF over-constrained tracking parallel mechanism. Firstly, the kinematic model is obtained by differential mapping of the topology models based on finite and instantaneous screws (FIS). Referred to virtual work principle, dynamic equation is constructed. To reduce the computation cost in practice, a linear model involving inertial and friction parameters is formulated for dynamic identification. A direct identification method is applied and all the parameters are obtained at the same time. Experiments are performed to show the accuracy and efficiency of the identification results. The contributions of the paper lie in 1) a concise method for constructing velocity and acceleration models of components is provided without tedious analysis 2) complete dynamic model of an over-constrained tracking parallel mechanism is developed 3) practical dynamic identification process is carried out compromising between precision and efficiency.

Dynamic parameter identification of upper‐limb rehabilitation robot system based on variable parameter particle swarm optimisation

To solve the problem of uncertain parameters in dynamic modelling of upper-limb rehabilitation robots, a dynamic parameter identification method based on variable parameters particle swarm optimisation (PSO) is developed. Based on the dynamic model of the system, the algorithm changes the inertia parameter and learning law of the basic PSO algorithm from the fixed-parameter to the function that changes with the number of iterations. It solves the problems of small search space in the early stage and slow convergence speed in the later stage of the basic PSO algorithm, which greatly improves its identification accuracy. Finally, through the comparison and analysis of the simulation results, compared with those of the least square (LS) and unmodified PSO identification algorithms, a great improvement in the identification accuracy of the algorithm is achieved. The control effect in the actual control system is also much better than those of the LS and PSO algorithms.

Population Dynamics of Suez Pufferfish (Lagocephalus suezensis) in Iskenderun Bay

Suez pufferfish (Lagocephalus suezensis) is a Lessepsian species introduced to the Mediterranean via the Suez Canal. This study was carried out for the identification of some population dynamics parameters of Suez pufferfish (Lagocephalus suezensis) distributed along the western coast of Iskenderun Bay. Samples were seasonally collected from two transects at 10 and 20 m depth contours from 2006 to 2017 using a commercial bottom trawl net with 44 mm mesh size. A total of 1279 individuals were investigated. Among them 427 were males, 586 were females, 266 were juveniles. Total length ranged from 5.2 to 24.0 cm and mean length was 13.44 ± 2.02 (± standard error) cm; total weight ranged from 1.77 to 232.97 g and mean weight was 32.05 ± 14.28 g for all individuals. Length-weight relationships were W=0.02*TL2.83 for males, W= 0.02*TL2.82 for females, and W=0.02*TL2.89 for aggregated data. The von Bertalanffy growth parameters were as follows; L∞ = 24.97 cm, K = 0.60 year-1, t0= -0.28, Rn = 0.145 for all specimens. Growth performance index was calculated as 5.92. Rates of total (Z) and natural (M) mortality was found to be 1.07 and 0.981 year-1.

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

To achieve precise trajectory tracking of robotic manipulators in complex environment, the precise dynamic model, parameters identification, nonlinear characteristics, and disturbances are the factors that should be solved. Although parameters identification and adaptive estimate method were proposed for robotic control in many literature studies, the essential factors, such as coupling and friction, are rarely mentioned as it is difficult to build the precise dynamic model of the robotic manipulator. An adaptive backstepping sliding mode control is proposed to solve the precise trajectory tracking under external disturbances with complex environment, and the dynamic response characteristics of a two-link robotic manipulator are described in this paper. First, the Lagrange kinetic method is used to derive the precise dynamic model which includes the nonlinear factor with friction and coupling. Moreover, the dynamic model of two-link robotic manipulator is built. Second, the estimate function for the nonlinear part is selected, and backstepping algorithm is used for analyzing the stabilities of the sliding mode controller by using Lyapunov theory. Furthermore, the convergence of the proposed controller is verified subject to the external disturbance. At last, numerical simulation results are reported to demonstrate the effectiveness of the proposed method.

Identification of the Dynamic Parameters of a Parallel Kinematics Mechanism with Prismatic Joints by Considering Varying Friction

This study proposed a novel approach for the offline dynamic parameter identification of parallel kinematics mechanisms in which the friction is significant and varying. Since the friction is significant, it should be incorporated to provide an accurate dynamic model. Furthermore, the varying normal forces as a result of the changing posture of the mechanism lead to varying friction forces, specifically varying static and Coulomb friction forces. By considering this variation, the static and Coulomb friction parameters are identified as coefficients instead of forces. A bound-constrained optimization technique using an iterative global search tool was employed in this work to minimize the residual errors while maintaining the physical feasibility of the solutions. Moreover, the friction was modeled by using the nonlinear Stribeck friction model since a linear friction model was not sufficient, whereas the variation of the friction followed the variation of the normal forces, which were evaluated through the Lagrange multipliers in the constrained dynamic model of the mechanism. The solutions obtained were verified by using some trajectories that were different from those used in the identification.

Experimental and Numerical Study on Dynamics of Two Footbridges with Different Shapes of Girders

The paper presents the experimental and numerical results of the dynamic system identification and verification of the behavior of two footbridges in Poland. The experimental part of the study involved vibration testing under different scenarios of human-induced load, impulse load, and excitations induced by vibration exciter. Based on the results obtained, the identification of dynamic parameters of the footbridges was performed using the peak-picking method. With the impulse load applied to both structures, determination of their natural vibration frequencies was possible. Then, based on the design drawings, detailed finite element method (FEM) models were developed, and the numerical analyses were carried out. The comparison between experimental and numerical results obtained from the modal analysis showed a good agreement. The results also indicated that both structures under investigation have the first natural bending frequency of the deck in the range of human-induced excitation. Therefore, the risk of excessive structural vibrations caused by pedestrian loading was then analysed for both structures. The vibration comfort criteria for both footbridges were checked according to Sétra guidelines. In the case of the first footbridge, the results showed that the comfort criteria are fulfilled, regardless of the type of load. For the second footbridge, it was emphasized that the structure meets the assumptions of the guidelines for vibration severability in normal use; nevertheless, it is susceptible to excitations induced by synchronized users, even in the case of a small group of pedestrians.

Parameter identification of nonlinear system via a dynamic frequency approach and its energy harvester application

A dynamic frequency-based parameter identification approach is applied for the nonlinear system with periodic responses. Starting from the energy equation, the presented method uses a dynamic frequency to precisely obtain the analytical limit cycle expression of nonlinear system and utilizes it as the mathematic foundation for parameter identification. Distinguished from the time-domain approaches, the strategy of using limit cycle to describe the system response is unaffected by the influence of phase change. The analytical expression is fitted with the value sets from phase coordinates measured in periodic oscillation of the nonlinear systems, and the unknown parameters are identified with the interior-reflective Newton method. Then the performance of this identification methodology is verified by an oscillator with nonlinear stiffness and damping. Besides, numerical simulations under noisy environment also verify the efficiency and robustness of the identification procedure. Finally, we apply this parameter identification method to the modeling of a large-amplitude energy harvester, to improve the accuracy of mechanical modeling. Not surprisingly, good agreement is achieved between the experimental data and identified parameters. It also verifies that the proposed approach is less time-consuming and more accuracy in identification procedure.

Experimental Parameter Identification for Moving Base Nonholonomic Robot Consisting Primary Joints

Main aim of this paper is to study dynamic modeling and parameter identification of a nonholonomic moving based robot with chain of serial manipulators with primary joints. One of the main scenarios for analyzing mechanical systems including the no holonomic constraints will be carried out by using Lagrangian formulation and its associated “Lagrange multipliers”. Most research on movable manipulators are limited to robots with only rotating joints. The dynamic equations of robots with rotational and prismatic movement is a very important subject with many applications. The combination of this system with a movable base will bring capability of operating in a wider area than a fixed base manipulator. The moving ability added to the agility of the manipulator, is requirement of many application such as; space explores, rescue operations, operation in hazardous locations, agriculture, and so on. Eliminating these variables from the obtained equations is a time-consuming and cumbersome task. To prevent computing the Lagrange multipliers associated with the nonholonomic constraints; Gibbs-Appell formulation shall be implemented. To automatically derive the motion equations and upgrade the computational efficiency, a recursive algorithm has been derived in the simulation of the system. In the concept of this algorithm, all the mathematical scheme is carried out by only 3 × 3 and 3 × 1 matrices. In the last section, computational modeling for a chain of the manipulator with 3 links and primary joints in each arm is performed to show the aptitude of the proposed scheme in implementation of the motion equations and parametric analysis for complex systems at this level. One of the main purposes of this study is to develop rover technologies for interplanetary explorations.

A novel approach for experimental identification of vehicle dynamic parameters

Pervasive applications of the vehicle simulation technology are a powerful motivation for the development of modern automobile industry. As basic parameters of road vehicle, vehicle dynamic parameters can significantly influence the ride comfort and dynamics of vehicle, and therefore have to be calculated accurately to obtain reliable vehicle simulation results. Aiming to develop a general solution, which is applicable to diverse test rigs with different mechanisms, a novel model-based parameter identification approach using optimized excitation trajectory is proposed in this paper to identify the vehicle dynamic parameters precisely and efficiently. The proposed approach is first verified against a virtual test rig using a universal mechanism. The simulation verification consists of four sections: (a) kinematic analysis, including the analysis of forward/inverse kinematic and singularity architecture; (b) dynamic modeling, in which three kinds of dynamic modeling method are used to derive the dynamic models for parameter identification; (c) trajectory optimization, which aims to search for the optimal trajectory to minimize the sensitivity of parameter identification to measurement noise; and (d) multibody simulation, by which vehicle dynamic parameters are identified based on the virtual test rig in the simulation environment. In addition to the simulation verification, the proposed parameter identification approach is applied to the real test rig (vehicle inertia measuring machine) in laboratory subsequently. Despite the mechanism difference between the virtual test rig and vehicle inertia measuring machine, this approach has shown an excellent portability. The experimental results indicate that the proposed parameter identification approach can effectively identify the vehicle dynamic parameters without a high requirement of movement accuracy.

Structural dynamic nonlinear model and parameter identification based on the stiffness and damping marginal curves

This paper presents a novel structural dynamic nonlinear model and parameter identification method based on the stiffness and damping marginal curves. The stiffness and damping marginal curves are first extracted from the structural dynamic responses. Then, nine nonlinear feature indices (NFIs) are defined based on the marginal curves to describe the characteristics of various nonlinear models. To reduce the dimension of NFIs and facilitate the subsequent calculation of the support vector machine (SVM), the principal component analysis (PCA) is implemented. Afterwards, the NFIs processed by PCA are employed as the training samples to SVM classifier, which is used to identify the structural nonlinear model. According to the identified nonlinear model corresponding to the type of nonlinearity, the parameters of the nonlinear model are further identified by the nonlinear least square method. The numerical results of a single degree of freedom (SDOF) system and a cantilever beam structure demonstrate that the proposed method can effectively identify both nonlinear model and corresponding parameters under various excitations even considering the noise effect. Subsequently, the proposed method is also verified by a benchmark system subjected to a semisinusoidal pulse load and a beam bridge structure subjected to an earthquake load. The proposed method is also applied to the experimental test of a bolted joint cantilever beam subjected to a random excitation in the laboratory. Both numerical and test results demonstrate that the proposed method can identify both nonlinear model and corresponding parameters within good accuracy and robustness.

Dynamic Parameter Design Method and Application under a General Linear Relationship between Response Variables and Signal Factors

In dynamic parameter design, there is often a different functional relationship between response variables and signal factors. SNR is an important tool for the parameter design of dynamic characteristics, and its estimation results change with the change of function relationships. This paper describes the dynamic parameter design method in a linear relationship between response variables and signal factors, and provides a case for verification. The results of this study are applicable to engineers and academics who need to conduct product design, process development and process optimization through dynamic parameter design.

A New Dynamic Parameters V-RC Model of Battery Parameter Identification

Different state of charge (SOC), batteries have different model parameters. This paper proposes a dynamic parameter identification method for battery models under different SOC. Firstly, dynamic parameter V-RC model of battery was designed according to the hysteresis loops characteristics and the polarization characteristics of the battery voltage during the charge-discharge process. Next, the test program was designed which can complete the lithium battery charge and discharge data acquisition, including identification of battery model parameters for AC signal injection. The relationship between OCV and SOC are obtained by using the least squares curve fitting method, which realizes the function fitting of the model’s parameters. Then, battery transient response experiment indicating that have different parameters under different SOC. Finally, test results can show that open-circuit voltage (OCV) error of the new model is lower than constant parameter model OCV error. The error of the fixed parameter model OCV is 2.6%, while the OCV error of the dynamic parameter model is 1.6%. Dynamic parameter VRC model can more accurately reflect the actual state of the battery.

Construction of approximate reasoning model for dynamic CPS network and system parameter identification

CPS (Cyber Physical System) is a large and complex real-time feedback system that integrates computing processes, physical processes, communication networks, sensor networks, and control systems. It has the powerful function of sensing and controlling the physical environment, which is a big wave following the Internet technology. Because the forms of communication, interaction, and collaboration between heterogeneous units within the CPS are intricate and complex, a comprehensive model needs to be established to describe and analyze the CPS. This paper analyzes the CPS architecture and proposes a new and more complete CPS architecture, decomposes according to this architecture, and classifies the physical entities in the CPS. At the same time, event-based modeling thinking is used to define, classify and formalize event messages. Considering the higher real-time requirements of CPS, an event weighting algorithm was designed according to the different priorities of real-time events. In order to reduce the congestion caused by the limited network bandwidth in the CPS system, improve the ability to identify abnormal data with great uncertainty, and fully guarantee the response rate of the CPS system to emergencies, this paper analyzes the complexity of the CPS system from the perspective of information theory. The average dynamic complexity of the CPS system is set as a threshold to determine the level of information entropy of the sensor data in a certain period of time. The CPS system selects high information entropy data to send first. The effectiveness is analyzed through experiments.