Optimal robot excitation and identification

Abstract

This paper discusses experimental robot identification based on a statistical framework. It presents a new approach toward the design of optimal robot excitation trajectories, and formulates the maximum-likelihood estimation of dynamic robot model parameters. The differences between the new design approach and the existing approaches lie in the parameterization of the excitation trajectory and in the optimization criterion. The excitation trajectory for each joint is a finite Fourier series. This approach guarantees periodic excitation which is advantageous because it allows: 1) time-domain data averaging; 2) estimation of the characteristics of the measurement noise, which is valuable in case of maximum-likelihood parameter estimation. In addition, the use of finite Fourier series allows calculation of the joint velocities and accelerations in an analytic way from the measured position response, and allows specification of the bandwidth of the excitation trajectories. The optimization criterion is the uncertainty on the estimated parameters or a lower bound for it, instead of the often used condition of the parameter estimation problem. Simulations show that this criterion yields parameter estimates with smaller uncertainty bounds than trajectories optimized according to the classical criterion. Experiments on an industrial robot show that the presented trajectory design and maximum-likelihood parameter estimation approaches complement each other to make a practicable robot identification technique which yields accurate robot models.

Closed-loop identification of an industrial robot containing flexibilities

“…The choice of input signal is crucial in all types of identification for the quality of the identified model. In the area of robot identification this problem has been studied in, for example, Swevers et al (1997), where however identification of rigid robots is considered. Since the robot considered here contains nonlinearities (back-lash in gear boxes, static friction, etc) the choice of input signal is even more important.…”

Unfalsified adaptive control for manipulators with parameter uncertainties

“…Performance analysis as a function of the payload: from the no-load condition to the maximum of the payload interval Due to one performance difference between data-driven and model-based control techniques for manipulators, related to the need of a precise knowledge of its intrinsic parameters, we studied the robustness of the UAC applying disturbances (lack of precision in parameter estimation) to the inertial parameters of the model, based on the worst case scenario of parameter identification deviations of 1σ and 2σ confidence intervals presented by Swevers et al [5]. This is possible by taking advantage of the model-based characteristic of the computed torque controller applied in this work for the design of the candidate controller set of the UAC and the fictitious reference computation.…”

“…Traditional model-based strategies are often applied dealing with imprecise model identification or measurement of inertial and friction parameters [1], [2], [3], [4]. But, the identification or measurement of parameters is a demanding task, with difficulties on excitation functions, validation of results [5], [6], [7], [8] and possible unexpected performance of the plant [5]. Due to the increasing complexity of robot arm applications like lifting or lowering objects, picking up objects from shelves or helping people with personal care activities, careful evaluation of control performance in novel approaches becomes an issue of interest, considering the analysis of the ability to handle payloads in common daily situations due to sudden changes or unstructured environments [9], [10], [11].…”

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

“…In order to satisfy the condition described above, a number of trajectories have been identified and developed based on Fourier series, which have been studied in robotics literature rather extensively, see Swevers et al (1996Swevers et al ( , 1997Swevers et al ( , 2007b) and Park (2006). The actual Fourier series that was used to generate excitation trajectory is given in equation (4).…”