Physically-Consistent Full Dynamic Parameter Identification Based on Nonlinear Friction Model

This article is concerned with the identification of industrial robot dynamics parameters, and an identification method based on a nonlinear friction model and a least squares (LS) with symbiotic organisms search (SOS) algorithm is proposed. First, according to the friction characteristics of robot joints, for the nonlinear Tustin friction model, two parameters are introduced to describe the Stribeck characteristics of Coulomb friction and static friction. Then, the joint velocity trajectory that can offset the nonfriction torque during the forward and reverse rotation is designed, and friction parameters are estimated by using LS. Based on the optimal criterion constructed by Hadamard’s inequality, the excitation trajectory represented by the Fourier series is designed, and the combined inertial parameter set is estimated by using LS. Second, select the minimum inertial parameters and friction parameters (minimum dynamic parameters) as the center points to search the upper and lower bounds; design the fitness function with adjustable weights of each joint and use the SOS algorithm to estimate the minimum dynamic parameter set as a whole. Finally, a variety of parameter identification algorithms are applied to the Staubli TX-90 robot under the same excitation trajectory. The experiment results show that the root-mean-square (rms) error of the joint torque estimated by the proposed method is the smallest. By executing the verification trajectory, the experiment results show that the rms error of the joint torque is small and the correlation coefficient is close to 1, thereby verifying the effectiveness of the estimated model for different motion trajectories.

Due to friction characteristics of clutch, the driveline is prone to cause a judder during vehicle starting, and then to cause the vehicle body to vibrate, which affects driving quality. In order to analyze the judder phenomenon, a nonlinear numerical friction model based on the Gaussian friction model is established in this paper. For the driveline of a front-wheel-drive vehicle, a five-degree-of-freedom (5DOF) lumped parameter model including a nonlinear friction element is established. The complex mode of the driveline during the clutch in slip condition is calculated. The key parameters affecting the driveline stability are analyzed. The self-excited judder and pressure-induced judder of the driveline are numerically simulated, and the corresponding causes are analyzed. The nonlinear friction torque of the clutch is also calculated. Furthermore, the effects of the key parameters such as the torsional stiffness and damping of the clutch and drive shaft suppressing the self-excited judder and pressure-induced judder are numerically studied respectively. Compared with the widely used Karnopp friction model, the nonlinear numerical friction model established in this paper comprehensively includes the stribeck effect in slip and the friction torque characteristics in stick. The phenomena of the judder and stick-slip of the driveline during vehicle starting are more accurately simulated. The simulation results are in good agreement with the experimental results, which verify the accuracy and effectiveness of the dynamic model including the nonlinear friction element established in this paper.

Currently, four methods are mainly used to evaluate the parameters of an asynchronous motor with a short-circuited rotor(AD with a short-circuited rotor): according to catalog data, according to test results, preliminary parameter estimation and dynamic identification. Each of the above methods has its advantages and disadvantages, however, the only method that allows you to evaluate the parameters of the AD model with a short-circuited rotor without stopping the process is the method of dynamic parameter identification. The advantages of the method of dynamic identification of parameters of the asynchronous motor replacement circuit (AD), in addition to the absence of the need to stop the technological process, include the ability to evaluate the parameters of the AD model taking into account its dynamically changing temperature state. In turn, the disadvantage of this method of parameter estimation is the need to build a complex identification procedure, which requires taking into account many factors and does not have a generally recognized mathematical description. In connection with the identified problems, it can be argued that expanding the arsenal of tools that allow determining blood pressure parameters without stopping the technological process is an urgent and timely task

Permanent magnet synchronous motor is widely used because of its reliable operation and high efficiency. The accuracy of parameter identification has an important impact on the operating performance and control methods of PMSM. Most of the current parameter identification methods fail to consider the effect of temperature on parameters, resulting in low parameter identification accuracy and poor control performance. Therefore, this paper proposes a mathematical model of permanent magnet synchronous motor considering the temperature change, and combines with the dynamic forgetting factor recursive least squares parameter identification method on this basis. The simulation results show that the proposed method can effectively monitor and identify the motor parameters, improve the accuracy of parameter identification and reduce the identification error.

Identification of dynamic parameters of a 3-DOF RPS parallel manipulator

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

In this paper, a method of vertical dynamic parameters identification based on flexible load is proposed for the deepsea application. The vertical dynamic parameters of the robot can be calculated at a depth of 11,000 meters. In this paper, the vertical dynamical identification experiment of HADAL ARV is introduced on the seabed near 11000 meters. Finally, the real-time residual buoyancy of the robot is calculated according to the obtained dynamic parameters, which verifies the effectiveness of the method.

In this paper, a hybrid optimization algorithm is proposed to identify the dynamic parameters of a 6-DOF electro-hydraulic parallel platform. The dynamic model of a parallel platform with arbitrary geometry, inertia distribution and frictions is obtained based on a structured Boltzmann–Hamel–d’Alembert formulation, and then the estimation equations are explicitly expressed in terms of a linear form with respect to the identified inertial and the friction coefficients in accordance with a linear friction model. However, when nonlinear friction models are considered, the parameter identification of the electro-hydraulic parallel platform is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and then an effective combination of the particle swarm optimization (PSO) method and the local quasi-Newton method is proposed to solve the identification problem. Experimental identification processes are carried out for the identified parameters, and the identified models are compared by the predicted forces between the LS method and the optimization technique as well as between the linear and nonlinear friction models.

The three-phase short-circuit test is a common method for measuring transient parameters of large synchronous machines. The experimental process is complex and carries certain risks. Traditional stationary test methods can enhance the safety of parameter identification experiments; however, the required rotor pre-positioning for these experiments is difficult to implement in large synchronous machines. To address these issues, this paper proposes a synchronous machine parameter identification method based on a three-phase connection of the DC decay test at arbitrary rotor positions, grounded on the principle of signal input experiments. This method equivalently transforms the DC decay test of the three-phase connection at arbitrary positions into independent rotor d- and q-axis pre-positioned DC decay tests. Using the simulated annealing particle swarm optimization algorithm to fit the current response signal, the machine's dynamic parameters are calculated. The validity and accuracy of the proposed parameter identification method were verified through experiments on a 172 MV A synchronous machine. This method requires low testing equipment, involves fewer tests, and has simple data processing, making it safe and reliable.

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.

In this paper a new method of modeling nonlinear friction as a function of both position and velocity is introduced. The proposed empirical model uses spectral analysis to identify the main sources of position dependent friction in complex mechanisms. In addition, the model describes the contribution to the overall friction of every moving part of the mechanism. In the paper, the proposed spectral-based modeling technique is used to describe static friction and sliding friction (which includes negative damping friction) of the first joint of PUMA 560 robot. This is the first friction model truly capable of describing nonlinear friction in complex mechanical systems.KeywordsAngular VelocityStatic FrictionFriction TorqueFriction CharacteristicNonlinear FrictionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An improved variable forgetting factor sliding window recursive least square-chaotic firefly optimization method for key dynamic parameters identification of lithium-ion batteries with hybrid electrochemical empirical and circuit modeling

Dynamic modeling and parameter identification for a gantry-type automated fiber placement machine combined with hydraulic balance system and nonlinear friction

This paper concerns the problem of dynamical identification for an industrial robot manipulator and presents an identification procedure based on an improved cuckoo search algorithm. Firstly, a dynamical model of a 6-DOF industrial serial robot has been derived. And a nonlinear friction model is added to describe the friction characteristic at motion reversal. Secondly, we use a cuckoo search algorithm to identify the unknown parameters. To enhance the performance of the original algorithm, both chaotic operator and emotion operator are employed to help the algorithm jump out of local optimum. Then, the proposed algorithm has been implemented on the first three joints of the ER-16 robot manipulator through an identification experiment. The results show that (1) the proposed algorithm has higher identification accuracy over the cuckoo search algorithm or particle swarm optimization algorithm and (2) compared to linear friction model the nonlinear model can describe the friction characteristic of joints better.

Offline robot dynamic identification methods are based on the use of the inverse dynamic identification model (IDIM), which calculates the joint forces/torques (estimated as the product of the known control signal (the input reference of the motor current loop) with the joint drive gains) that are linear in relation to the dynamic parameters, and on the use of the linear least squares technique to calculate the parameters (IDIM-LS technique). However, as actuation-redundant parallel robots are overactuated, their IDIM has an infinity of solution for the force/torque prediction, depending on the value of the desired overconstraint that is a priori unknown in the identification process. As a result, the IDIM cannot be used as it is for such a class of parallel robots. This paper proposes a procedure for the dynamic parameter identification of actuation-redundant parallel robots. The procedure takes advantage of two possible modified formulations for the IDIM of actuation-redundant robots that can be used for identification purpose. The modified IDIM formulations project some or all input torques/forces onto the robot bodies, thus leading to a unique solution of the model that can then be used in the identification process. A systematic and straightforward way to compute these modified IDIM is presented. The identification of the inertial parameters of a planar parallel robot with actuation redundancy, the DualV, is then carried out using these modified IDIM. Experimental results show the validity of the methods.