An instrumental variable method for robot identification based on time variable parameter estimation

This paper considers the data-based identification of industrial robots using an instrumental variable method that uses off-line estimation of the joint velocities and acceleration signals based only on the measurement of the joint positions. The usual approach to this problem relies on a ‘tailor-made’ prefiltering procedure for estimating the derivatives that depends on good prior knowledge of the system’s bandwidth. The paper describes an alternative Integrated Random Walk SMoothing (IRWSM) method that is more robust to deficiencies in such a priori knowledge and exploits an optimal recursive algorithm based on a simple integrated random walk model and a Kalman filter with associated fixed interval smoothing. The resultant IDIM-IV instrumental variable method, using this approach to signal generation, is evaluated by its application to an industrial robot arm and comparison with previously proposed methods

Refined Instrumental Variable method for non-linear dynamic identification of robots

The identification of the dynamic parameters of robot is based on the use of the inverse dynamic identification model which is linear with respect to the parameters. This model is sampled while the robot is tracking “exciting” trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the bandpass filtering of the joint position at high sample rate. So, the observation matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS estimator is not consistent. This paper focuses on the Refined Instrumental Variable (RIV) approach to over-come this problem of noisy observation matrix. This technique is applied to a 2 degrees of freedom (DOF) prototype devel-oped by the IRCCyN Robotic team

State Space Estimation Method for Robot Identification

In this paper, we study the identification of robot dynamic models. The usual technique, based on the Least-Squares method, is carefully detailed. A new procedure based on Kalman filtering and fixed interval smoothing is developed. This new technique is compared to usual one with simulated and experimental data. The obtained results show that the proposed technique is a credible alternative, especially if the system bandwidth is unknown

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

Proportional-integral-plus control applications of state-dependent parameter models

This paper considers proportional-integral-plus (PIP) control of non-linear systems defined by state-dependent parameter models, with particular emphasis on three practical demonstrators: a microclimate test chamber, a 1/5th-scale laboratory representation of an intelligent excavator, and a full-scale (commercial) vibrolance system used for ground improvement on a construction site. In each case, the system is represented using a quasi-linear state-dependent parameter (SDP) model structure, in which the parameters are functionally dependent on other variables in the system. The approach yields novel SDP-PIP control algorithms with improved performance and robustness in comparison with conventional linear PIP control. In particular, the new approach better handles the large disturbances and other non-linearities typical in the application areas considered

A New Recursive Instrumental Variables Approach for Robot Identification

International audienceThe 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

State space estimation method for the identification of an industrial robot arm

In this paper, we study the identification of industrial robot dynamic models. Since the models are linear with respect to the parameters, the usual identification technique is based on the Least-Squares method. That requires a careful preprocessing of the data to obtain consistent estimates. In this paper, we carefully detail this process and propose a new procedure based on Kalman filtering and fixed interval smoothing. This new technique is compared to usual one with experimental data considering an industrial robot arm. The obtained results show that the proposed technique is a credible alternative, especially if the system bandwidth is unknown

Proportional-Integral-Plus Control Strategy of an Intelligent Excavator

This article considers the application of Proportional-Integral-Plus (PIP) control to the Lancaster University Computerised Intelligent Excavator (LUCIE), which is being developed to dig foundation trenches on a building site. Previous work using LUCIE was based on the ubiquitous PI/PID control algorithm, tuned on-line, and implemented in a rather ad hoc manner. By contrast, the present research utilizes new hardware and advanced model-based control system design methods to improve the joint control and so provide smoother, more accurate movement of the excavator arm. In this article, a novel nonlinear simulation model of the system is developed for MATLAB/SIMULINK, allowing for straightforward refinement of the control algorithm and initial evaluation. The PIP controller is compared with a conventionally tuned PID algorithm, with the final designs implemented on-line for the control of dipper angle. The simulated responses and preliminary implementation results demonstrate the feasibility of the approach