Dynamic Parameter Identification Method Research Articles

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

The magnetic coupling wireless charging system may experience changes in mutual inductance and load due to the impact of actual underwater environment, which would lower the system’s transmission efficiency. In response to the circumstance, firstly, this paper proposes the circuit structure of the dynamic autonomous underwater vehicle wireless charging system with LCC-S-Buck-Boost, and a dynamic parameter identification method based on the multi-strategy nonlinear Rime algorithm. Secondly, it is proposed that the difference between the actual input current value of the primary side of the system and the current value calculated by the identification method, and the difference between the actual coil current value of the secondary side of the system and the coil current value calculated by the identification method are used as the adaptability function of the identification method, and the construction of the actual model of the system and the identification algorithm has been completed, and the results of the identification of the system’s mutual inductance and the equivalent load have been obtained. Finally, based on the identification results, it is confirmed that the proposed identification method is efficient and accurate in identifying system mutual inductance and equivalent loads in complex underwater environments by comparing with other algorithms under different values of mutual inductance and equivalent loads.

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 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 parameter identification based on improved particle swarm optimization and comprehensive excitation trajectory for 6R robotic arm

PurposeRobotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.Design/methodology/approachThis study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.FindingsExperiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.Originality/valueA new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

Dynamic parameter identification and adaptive control with trajectory scaling for robot-environment interaction.

To improve the force/position control performance of robots in contact with the environment, this paper proposes a control scheme comprising dynamic parameter identification, trajectory scaling, and computed-torque control based on adaptive parameter estimation. Based on the Newton-Euler method, the dynamic equation and its regression matrix is obtained, which is helpful to reduce the order of the model. Subsequently, the least-square method is implemented to calculate the values of the basic parameters of the dynamics. The identified dynamic parameters are used as initial values in the adaptive parameter estimation to obtain the torque, and trajectory scaling is applied to control the contact force between the robot and the environment. Finally, the dynamic parameter identification method and control algorithm are verified by conducting a simulation. The results show that the comprehensive application can help improve the control performance of robots.

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

The identification method of reservoir parameters has the problems of low recognition accuracy and timeliness. A dynamic parameter identification method of shallow reservoir based on Kalman filter is proposed. The history fitting method is used to establish and adjust the shallow reservoir model, and the parameters and range of the reservoir model are continuously adjusted according to the actual observation data of the shallow reservoir. Kalman filter is used to filter the data of shallow reservoir and to filter out the noise and interference information. Then the dynamic parameters of shallow reservoir are identified by the method of water resistivity shale content discrimination, and the state of shallow reservoir is reflected by the shallow water resistivity. The comparison shows that the average recognition accuracy of the method can reach 95.2%, the recognition process takes only 22 seconds at most, and its recall precision value level is always high.

An accurate identification method based on double weighting for inertial parameters of robot payloads

AbstractThe inertial parameters of payloads attached to the end effector of robots benefit to several robotics applications, such as the model-based control, the task optimization, and so on. These applications require the accurate estimation of the inertial parameters. In the existing payload estimation approaches, however, the data weighting technique, which can reduce the adverse effects of outliers and significantly improve the final results, has not been applied yet. In this article, an accurate identification method based on double weighting for inertial parameters of robot payloads is proposed. In order to obtain the weighting matrices, a modified dynamic parameter identification method with two loops is firstly proposed. Then, based on the identified results of dynamic parameters, a payload identification model based on double weighting is constructed. In addition, the variations of both nonlinear friction parameters and linear friction parameters caused by the payload are considered in this model. Finally, experimental comparisons between our method and another four methods are conducted. The results confirm that our method shows the best performance, especially on improving the identification accuracy of mass and center of mass of the payload.

Detection and Analysis of Bionic Motion Pose of Single Leg and Hip Joint Based on Random Process.

Based on the spectral representation method of random function and combined with memoryless nonlinear translation theory, this paper analyzes the transformation relationship between potential Gaussian random process and non-Gaussian random process, and successfully generates a stationary non-Gaussian random process that conforms to the target non-Gaussian random process. For the non-stationary non-Gaussian random process simulation, on the basis of the stationary Gaussian random process, the intensity non-stationary uniform modulation model is used to modulate it, and combined with the nonlinear translation theory, the non-stationary non-Gaussian random process conforming to the target non-Gaussian random process is obtained. Aiming at the single-leg bouncing model based on the flexible rotary hip joint, the stability of its bouncing motion under passive motion is studied, and the influence of the flexible hip rotary joint on the motion stability is analyzed by comparing the single-leg bouncing motion characteristics of the free rotary hip joint. Based on the inverse dynamic control of the air phase, the fixed point distribution of the single-leg bounce of the flexible rotary hip joint was improved, and the function of the flexible rotary hip joint in the energy conversion of the bouncing motion was studied by establishing the energy consumption evaluation function. The kinematic performance verification, dynamic performance verification, dynamic parameter identification verification, and modal experiment simulation analysis were carried out for the built experimental platform, and the comparison and analysis with its theoretical model were carried out. The results show that the theoretical motion trajectory of the test mobile platform is basically consistent with the actual motion trajectory in the X and Y directions, and there is a small error in the Z-axis direction, and the error is within an acceptable range, indicating that the experimental platform system can be used to simulate the human hip joint. There is a large error between the theoretical value of the driving torque calculated by the theoretical value of the dynamic parameters and the measured value, and the dynamic theoretical model cannot accurately predict the driving torque. The predicted value of the driving torque calculated by using the identification value of the dynamic parameters is in good agreement with the measured torque, and its confidence is increased by 10–16%, indicating that the dynamic parameter identification method in this paper has a high degree of confidence.

A novel parameter identification method for flexible-joint robots using input torque and motor-side motion data

AbstractThe traditional identification methods of industrial robots are based on Inverse Dynamic Identification Model (IDIM). Based on the model, input torque, motor-side and link-side motion data are necessary when joint flexibilities are considered. However, it is often unavailable or expensive to general robots which are not equipped with link-side sensors. To solve the problem, a novel dynamic parameter identification method, which only employ input torque and motor-side motion data, is proposed in this report. Based on motor-side dynamics, link-side dynamics are modified as high-order nonlinear functions of input torque and motor-side motion. Then, through different trajectory-load groups and high-order observers, the nonlinear equations can be solved, and dynamic parameters can be estimated with short operation time. The selection rules of the trajectory-load groups are then discussed based on simulation results, so as to promote estimation results. Finally, experiments are conducted to verify the proposed method and exhibit the selection rules of observer gains. As shown in the report, except viscous friction parameters, identification difference between the IDM-based methods and the proposed one is less than 9%.

An improved parameter identification method of redundant manipulator

To achieve more accurate simulation and control in the use of the manipulator, it is necessary to establish an accurate dynamic model of the redundant manipulator. The research of this article focuses on the dynamic parameter identification method of the redundant manipulator. In the study, the spinor theory is applied to the Newton–Euler dynamic equation, the Coulomb + viscous friction model is adopted, and the minimum parameter set is obtained by linearization derivation. The parameter identification of the manipulator is realized using the method of offline identification of the measured current, and the coefficient of the excitation trajectory is optimized using the nonlinear optimization function. Finally, the parameter set with high accuracy is obtained, and the motion trajectory of each joint can be obtained. The scheme has high accuracy and can meet the needs of practical application. To verify the accuracy and reliability of this method, we have carried out experiments on a service robot “Walker” and obtained the desired results.

Human-Exoskeleton Coupling Dynamics of a Multi-Mode Therapeutic Exoskeleton for Upper Limb Rehabilitation Training

The purpose of this study is to establish the human-exoskeleton coupling (HEC) dynamic model of the upper limb exoskeleton, overcome the difficulties of dynamic modeling caused by the differences of individual and disease conditions and the complexity of musculoskeletal system, to achieve early intervention and optimal assistance for stroke patients. This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient’s height and weight and HEC dynamics parameters in the PPT mode. The RMSE of the elbow and shoulder joints of the prediction model is 9.5% and 13.3%. The proposed HEC dynamic model is helpful to provide different training effects in the PAT and PPT mode and optimal training and assistance for stroke patients.

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

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.

Dynamic parameters identification of a haptic interface for a helicopter flight simulator

Abstract. The haptic interface force feedback is one of the key factors for a reliable flight simulation. This paper addresses the design and control implementation of a simple joystick-like haptic interface to be used for a helicopter flight simulator. The expression of the haptic interface force is obtained by dynamic analysis of the haptic interface operation. This paper proposes a new strategy aiming at avoiding the use of an expansive and complex force/torque sensor. Accordingly, specific dynamic model is implemented by including Stribeck friction to describe the friction moment. Experimental data are processed as based on a genetic algorithm for identifying the dynamic parameters in the Stribeck friction model. This allows to obtain the friction moment parameters of the haptic interface, as well as the torque distribution due to gravity and the rotational inertia parameters of the haptic interface for the calculation of the haptic interface force. Experimental tests are carried out and results are used to validate the proposed dynamic model and dynamic parameter identification method and demonstrate the effectiveness of the proposed force feedback while using a cheap photoelectric sensor instead of an expansive force/torque sensor.

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.

A dynamic parameter identification method of industrial robots considering joint elasticity

Considering the joint elasticity, a novel dynamic parameter identification method is proposed for general industrial robots only with motor encoders. Firstly, the unknown parameters of the elastic joint dynamic model are analyzed and divided into two types. The first type is the motion-independent parameter only including the joint stiffness, which can be identified by the static force/torque-deformation experiments without the dynamic model. The second type is the motion-dependent parameter composed of the rest of the parameters, which needs the dynamic excitation experiments. Therefore, these two types of parameters can be identified separately. Meanwhile, it is found that the rotor inertia parameters can be obtained from the manufacturer, which reduces the identification difficulty of other parameters. After obtaining the rotor inertia and joint stiffness, an approximate processing algorithm is proposed considering the motor friction to establish the linear identification model of other parameters. Hence, the least squares can be employed to identify the parameters, and the independence of the inertia and joint viscous friction parameters are not affected. Meanwhile, the exciting trajectories can be optimized throughout the robot workspace, which reduces the effect of measurement noise on identification accuracy. With the proposed separated identification strategy and approximate processing algorithm, the dynamic parameters can be obtained precisely without double encoders on each joint. Finally, a series of simulations are conducted to evaluate the good performance of the proposed method.

A Dynamic Parameter Identification Method for Flexible Joints Based on Adaptive Control

This paper investigates the dynamic parameter identification for flexible joints without additional sensors. The estimation accuracy of traditional methods based on the original dynamic model is limited by measurement of angular acceleration. Recently, approaches using integral operations are raised to solve this problem. However, the integral operations easily result in error accumulation and deteriorate the parameter estimation accuracy. This study proposes a new dynamic parameter identification method for flexible joints. By constructing an alternative system function and estimating its outputs through nonmodel-based adaptive control, we avoid the measurement of angular acceleration and integral operations. On this basis, dynamic parameters of flexible joints are estimated using the recursive least squares (RLS) method. The experimental results indicate that the proposed method possesses better estimation accuracy than the general RLS- and integral-based methods. It is also validated that the proposed method can provide accurate estimation even under a relatively low-resolution system. Applications to model-based motion control and physical human-robot interaction are also verified.

A Flat Design and a Validated Model for an AUV Reconfigurable Magnetic Coupling Thruster

Reconfigurable Magnetic Coupling Thruster (RMCT) is an enabling technology for achieving vectorial thrust on AUVs. This allows us to dispose of dynamic, compliant, and secure propulsion to enhance their agility for demanding locomotion tasks. RMCT can generate and control a thrust vector by redirecting its propeller through the compliance of the magnetic coupling and therefore achieve thrust vectoring for AUVs. In this paper, a new design of RMCT is presented. This new prototype uses a flat geometry magnetic coupling for a better AUV full integrability. The new design provides some additional advantages with regard to its predecessor (spherical RMCT); namely, construction simplicity and better torque transmission. The first characteristic is derived from its flat shapes, which makes it easier to manufacture (cubic magnets) and to isolate (planar air gap between rotors). The second characteristic is due to the fact that now both parts of the magnetic coupling can store magnets (increasing the maximal transmitted torque). This paper proposes a thruster dynamics model, a magnetic coupling model, and shows the use of a dynamic parameter identification method based on algorithms used for robotic manipulators. The validity of the dynamic model and identified parameters is then analyzed by means of experimental tests.

Dynamic Parameter Identification of the Base of a Space Robot when Considering the Gravity Gradient

This paper presents a dynamic parameter identification method of the base of a free-flying space robot. Since the dynamic parameters play a significant role in the control of space robot system, it is necessary to identify the dynamic parameters of a space robot. First the dynamic parameters of the base are identified based on principle of momentum conservation in this paper. Then gravity gradient torque is used to identify the product of inertia for that it has effect on the attitude of a space robot. The principle and experimental process of the identification are introduced and the feasibility of the method is verified by simulation.

Dynamic Parameter Identification of Unknown Object Handled by Space Manipulator

This paper presents a dynamic parameter identification method of the unknown object handled by manipulator. Since the load will change the dynamic characteristic of space robot system, it is necessary to identify the dynamic parameters of the handled object. The dynamic parameters of the handled object are identified based on principle of momentum conservation in this paper. The principle and experimental process of the identification is introduced and the feasibility of the method is verified by simulation.