Inverse Dynamic Identification Model Research Articles

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%.

Sequential semidefinite optimization for physically and statistically consistent robot identification

This work considers the problem of dynamic identification for robotic mechanisms given noisy measurements of configuration variables and applied torques. Conventionally, this problem is solved via least-squares, exploiting linearity properties of the inverse dynamics model for rigid-body systems. However, the nonlinear dependency of this model on configurations and velocities gives rise to bias in the resultant estimates when using noisy or even filtered data. Further, these biases can cause parameters of best fit to be non-physical, potentially leading to an ill-posed forward dynamic model. The main contribution of this paper is to propose a sequential semidefinite optimization procedure to both (1) ensure the physical consistency of the identified model and (2) maintain the statistical consistency of the estimator. The new method validates both a direct and inverse dynamic identification model (DIDIM), and also ensures that intermediate iterates of the algorithm remain physically valid. Due to these favorable properties, the method is named a Physically-Consistent DIDIM (PC-DIDIM) approach. Recent statistical hypothesis tests for instrumental variable approaches are generalized for application with a PC-DIDIM approach. Experimental results with a six-degree-of-freedom industrial robot supported by Monte Carlo simulations show the effectiveness of the new method and robustness benefits in comparison to conventional least-squares and the vanilla DIDIM method.

Output Error Methods for Robot Identification

Abstract Industrial robot identification is usually based on the inverse dynamic identification model (IDIM) that comes from Newton's laws and has the advantage of being linear with respect to the parameters. Building the IDIM from the measurement signals allows the use of linear regression techniques like the least-squares (LS) or the instrumental variable (IV) for instance. Nonetheless, this involves a careful preprocessing to deal with sensor noise. An alternative in system identification is to consider an output error approach where the model's parameters are iteratively tuned in order to match the simulated model's output and the measured system's output. This paper proposes an extensive comparison of three different output error approaches in the context of robot identification. One of the main outcomes of this work is to show that choosing the input torque as target identification signal instead of the output position may lead to a gain in robustness versus modeling errors and noise and in computational time. Theoretical developments are illustrated on a six degrees-of-freedom rigid robot.

A novel two-step parameter identification method for GyroWheel system

GyroWheel is a rate sensing/momentum management instrument for small spacecrafts. The challenge to realize accurate angular rate estimation consists in the identification of system parameters, which is investigated in this paper. An inverse dynamic model is derived by ignoring the high-frequency components, and it is the foundation of parameter identification. Then a two-step method is developed to solve the ill-conditioned problem of parameter identification. In the first step, the inverse dynamic identification model is simplified in some special situations, and a rough estimation of the parameters can be obtained. In the second step, a constrained optimization problem is obtained by introducing the results of the first step, and particle swarm optimization algorithm is utilized to realize the identification of these parameters. Validation simulations and experiments on a GyroWheel prototype show that the proposed method is a good promising method for offline parameter identification.

Parameters identification of an electrical actuator with methods based on IDIM, DIDIM, IDIM2, DIDIM2

ABSTRACTThe IDIM (Inverse Dynamic Identification Models) method is based on a closed loop identification of the inverse model which is linear in relation to the parameters. By mixing the OE (Output Error) method and the IDIM technique, we obtain the method called DIDIM (Direct and Inverse Dynamic Identification Models). Experimental results on decoupled identification of the electrical dynamic and the mechanical dynamic have proven the efficiency of the IDIM and the DIDIM methods. But to perform a global identification of all the parameters, whatever the electrical machines (direct current motor, synchronous motor, asynchronous motor), it has been necessary to introduce two phases of calculations to get the parameters. This is why we have called them IDIM2 and DIDIM2. In this paper, we have presented the comparison of these four methods with experimentations on a DC motor, which has simple equations, to see easily nuances between IDIM, DIDIM, IDIM2, DIDIM2.

An improved instrumental variable method for industrial robot model identification

Industrial robots are electro-mechanical systems with double integrator behaviour, necessitating operation and model identification under closed-loop control conditions. The Inverse Dynamic Identification Model (IDIM) is a mechanical model based on Newton’s laws that has the advantage of being linear with respect to the parameters. Existing Instrumental Variable (IDIM-IV) estimation provides a robust solution to this estimation problem and the paper introduces an improved IDIM-PIV method that takes account of the additive noise characteristics by adding prefilters that provide lower variance estimates of the IDIM parameters. Inspired by the prefiltering approach used in optimal Refined Instrumental Variable (RIV) estimation, the IDIM-PIV method identifies the nonlinear physical model of the robot, as well as the noise model resulting from the feedback control system. It also has the advantage of providing a systematic prefiltering process, in contrast to that required for the previous IDIM-IV method. The issue of an unknown controller is also considered and resolved using existing parametric identification. The evaluation of the new estimation algorithms on a six degrees-of-freedom rigid robot shows that they improve statistical efficiency, with the controller either known or identified as an intrinsic part of the IDIM-PIV algorithm.

An automated instrumental variable method for rigid industrial robot identification

Industrial robots must be operated in closed-loop since they are electro-mechanical systems with double integrator behaviour. Their mechanical model, called the Inverse Dynamic Identification Model (IDIM), is based on Newton’s laws and has the advantage of being linear with respect to the parameters. The Instrumental Variable (IDIM-IV) method provides a robust solution to the closed-loop estimation problem. This method relies on a tailor-made prefiltering process in order to estimate accurate parameters. An alternative and automatic way of constructing the observation matrix has been recently introduced. If this methodology provides appropriate estimated parameters, it can fail to estimate the variances of those parameters. In this paper, an identification of the additive noise characteristics is included in the process to obtain correct and lower variances of the IDIM parameters. The evaluation of the new estimation algorithm on a one degree-of-freedom rigid robot shows that it improves statistical efficiency, while minimizing the a priori knowledge required from the practitioner.

A New Recursive Instrumental Variables Approach for Robot Identification

The work presented in this paper focus on robot identification and presents a method based on the use of instrumental variables (IV). When dealing with en-bloc and offline identification of robots, the instrumental matrix constructed with the inverse dynamic model (IDM) and simulated data obtained from the simulation of the direct dynamic model (DDM). In this paper, a new recursive IV approach relevant for robot identification is presented. The instrumental matrix is constructed with the IDM and the references and their derivatives which are previously filtered by the transfer function of the position closed loop. This new way of building the instrumental matrix avoids the simulation of the DDM and offers some perspectives for online identification and real-time implementation. This recursive IV method termed IDIM-RIV (Inverse Dynamic Identification Model Recursive Instrumental Variables) is experimentally validated on the two degrees-of-freedom SCARA robot. Finally, some hints for real-time implementation are provided.

Identification and control of electro-mechanical systems using state-dependent parameter estimation

ABSTRACTThis paper addresses the important topic of electro-mechanical systems identification with an application in robotics. The standard inverse dynamic identification model with least squares (IDIM-LS) method of identifying models for robotic systems is based on the use of a continuous-time inverse dynamic model whose parameters are identified from experimental data by linear LS estimation. The paper describes a new alternative but related approach that exploits the state-dependent parameter (SDP) method of nonlinear model estimation and compares its performance with that of IDIM-LS. The SDP method is a two-stage identification procedure able to identify the presence and graphical shape of nonlinearities in dynamic system models with a minimum of a priori assumptions. The performance of the SDP method is evaluated on two electro-mechanical systems: the electro-mechanical positioning system and the second link of the TX40 robot. The experimental results demonstrate how SDP identification helps to avoid over-reliance on prior conceptions about the nature of the nonlinear characteristics and correct any deficiencies in this regard. Finally, a simulation study shows how the resulting SDP model is able to facilitate nonlinear control system design using linear-like design procedures.

Dynamic Parameter Identification of Overactuated Parallel Robots

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.

Global Identification of Mechanical and Electrical Parameters of Asynchronous Motor Driven Joint with a Fast CLOE Method

In many cases of new actuation of compliant controlled or bio-inspired joint driven robot, a global identification of electrical and mechanical coupled dynamics is required. This paper proposes a technique which mixes a closed loop output error method with the inverse dynamic identification model method, which allows for the use of linear least-squares technique to estimate the parameters. A first approach which has been validated on a DC motor achieves a decoupled identification of the electrical and mechanical dynamics but fails to make a simultaneous identification. A major improvement of that method is proposed to carry out the coupled identification of both mechanical and electrical parameters. A validation on an asynchronous motor driven joint shows the effectiveness of the new procedure.

Comparison Between the CLOE Method and the DIDIM Method for Robots Identification

This brief deals with the identification of industrial robots. The usual identification method is based on the use of the inverse dynamic identification model and the least-squares technique. Another approach is the direct and inverse dynamic identification models (DIDIM) method, which is a closed-loop output error (CLOE) method minimizing the quadratic error between the actual and simulated joint torques. In this brief, the DIDIM method is compared with the usual CLOE method, which minimizes the quadratic error between the actual and simulated joint positions. The comparison is carried out on six degrees of freedom industrial robot. The experimental results show that the DIDIM method needs three iterations to identify 60 parameters while the usual CLOE method needs 20 iterations.

Global Identification of Joint Drive Gains and Dynamic Parameters of Robots

Off-line robot dynamic identification methods are based on the use of the inverse dynamic identification model (IDIM), which calculates the joint forces/torques that are linear in relation to the dynamic parameters, and on the use of linear least squares technique to calculate the parameters (IDIM-LS technique). The joint forces/torques are calculated as the product of the known control signal (the input reference of the motor current loop) by the joint drive gains. Then it is essential to get accurate values of joint drive gains to get accurate estimation of the motor torques and accurate identification of dynamic parameters. The previous works proposed to identify the gain of one joint at a time using data of each joint separately. This is a sequential procedure which accumulates errors from step to step. To overcome this drawback, this paper proposes a global identification of the drive gains of all joints and the dynamic parameters of all links. They are calculated altogether in a single step using all the data of all joints at the same time. The method is based on the total least squares solution of an overdetermined linear system obtained with the inverse dynamic model calculated with available input reference of the motor current loop and joint position sampled data while the robot is tracking some reference trajectories without load on the robot and some trajectories with a known payload fixed on the robot. The method is experimentally validated on an industrial Stäubli TX-40 robot.

A Generic Instrumental Variable Approach for Industrial Robot Identification

This paper deals with the important topic of industrial robot identification. The usual identification method is based on the inverse dynamic identification model and the least squares technique. This method has been successfully applied on several industrial robots. Good results can be obtained, provided a well tuned derivative band-pass filtering of joint positions is used to calculate the joint velocities and accelerations. However, one cannot be sure whether or not the band-pass filtering is well tuned. An alternative is the instrumental variable (IV) method, which is robust to data filtering and is statistically optimal. In this paper, a generic IV approach suitable for robot identification is proposed. The instrument set is the inverse dynamic model built from simulated data calculated from simulation of the direct dynamic model. The simulation is based on previous estimates and assumes the same reference trajectories and the same control structure for both actual and simulated robots. Finally, gains of the simulated controller are updated according to IV estimates to obtain a valid instrument set at each step of the algorithm. The proposed approach validates the inverse and direct dynamic models simultaneously, is not sensitive to initial conditions, and converges rapidly. Experimental results obtained on a six-degrees-of-freedom industrial robot show the effectiveness of this approach: 60 dynamic parameters are identified in three iterations.

Dynamic identification of the Kuka LWR robot using motor torques and joint torque sensors data

Off-line robot dynamic identification methods use the Inverse Dynamic Identification Model (IDIM), which calculates the motor torques that are linear in relation to the dynamic parameters of both links and drive chains, and use linear least squares technique (IDIM-LS technique). For most robots, the only available data are the motor position and the motor torques which are calculated as the product of the known current reference signal by the joint drive gains. Then the accuracy of links parameters may be limited by noise and error modeling in the drive chains. The Kuka LWR robot (industrial version IIWA: Intelligent Industrial Work Assistant) gives the possibility for an industrial robot to investigate this problem using the joint torque sensors data, measured at the output of the harmonic drive geared drive chains, to identify only the links inertial parameters without the errors coming from the drive chains. This paper focuses on the comparison of the accuracy of the identification of the dynamic parameters of the rigid model of the LWR4+ version, which is very popular in robotics research, using measures of the motor positions and the motor currents, or the torque sensors measurements or both side data. This paper is giving a first complete and reliable identified rigid dynamic model of the LWR4+, publicly available for the robotics community. Moreover, this work shows for the first time the strong result that motor torques calculated from motor currents can identify the links inertial parameters with the same accuracy than using joint torque sensors at the output of the joint drive chains.

Global identification of joint drive gains and dynamic parameters of parallel robots

Off-line 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 linear least squares technique to calculate the parameters (IDIM-LS technique). Most of the papers dealing with the dynamic parameters identification of parallel robots are based on simple models, which take only the dynamics of the moving platform into account. However, for advanced applications such as output force control, in which the robot interaction force with the environment are estimated from the values of the input reference, both identifications of the full robot model and joint drive gains are required to obtain the best results. In this paper a systematic way to derive the full dynamic identification model of parallel robots is proposed in combination with a method that allows the identification of both robot inertial parameters and drive gains. The method is based on the total least squares solution of an over-determined linear system obtained with the inverse dynamic model. This model is calculated with available input reference of the motor current loop and joint position sampled data while the robot is tracking some reference trajectories without load on the robot and some trajectories with a known payload fixed on the robot. The method is experimentally validated on a prototype of parallel robot, the Orthoglide.

Global Identification of Robot Drive Gains Parameters Using a Known Payload and Weighted Total Least Square Techniques

Off-line robot dynamic identification methods are based on the use of the Inverse Dynamic Identification Model (IDIM), which calculates the joint forces/torques that are linear in relation to the dynamic parameters, and on the use of linear least squares technique to calculate the parameters (IDIM-LS technique). The joint forces/torques are calculated as the product of the known control signal (the current reference) by the joint drive gains. Then it is essential to get accurate values of joint drive gains to get accurate identification of inertial parameters. In the previous works, it was proposed to identify each gain separately. This does not allow taking into account the dynamic coupling between the robot axes. In this paper the global joint drive gains parameters of all joints are calculated simultaneously. The method is based on the weighted total least squares solution of an over-determined linear system obtained with the inverse dynamic model calculated with available current reference and position sampled data while the robot is tracking one reference trajectory without load on the robot and one trajectory with a known payload fixed on the robot. The method is experimentally validated on an industrial 6 joint Stäubli TX-40 robot.

A new output error method for a decoupled identification of electrical and mechanical dynamic parameters of DC motor-driven robots

Abstract The identification methods based on the inverse dynamic model allow to use the linear least-squares technique to estimate the dynamic parameters (IDIL-LS method) of mechanical and electrical systems. Assuming a good data filtering to calculate derivatives and reference trajectories that excite the system dynamics, this method has been validated through the experimental identification. Recently, an output error (OE) avoids calculating the derivatives of actual data. This is possible by using the same reference trajectory and the same control law for the actual system and the simulated one. The optimal parameters minimize the 2-norm of the error between the actual output y and the simulated one ys. This nonlinear least-squares problem is dramatically simplified using the inverse dynamic model which is linear to the parameters. This method, called DIDIM (Direct and Inverse Dynamic Identification models), has been validated on robot's dynamic parameters identification. In this paper, DIDIM is applied to the decoupled identification of the electrical and mechanical dynamic parameters of DC motor-driven robots using only the reference control voltage of the PWM amplifier and the current reference of the current loop.