Stig Moberg

A Benchmark Problem for Robust Control of a Multivariable Nonlinear Flexible Manipulator

A benchmark problem for robust feedback control of a manipulator is presented. The system to be controlled is an uncertain nonlinear two link manipulator with elastic gear transmissions. The gear transmission is described by nonlinear friction and elasticity. The system is uncertain according to a parametric uncertainty description and due to uncertain disturbances affecting both the motors and the tool. The system should be controlled by a discrete-time controller that optimizes performance for given robustness requirements. The control problem concerns only disturbance rejection. The proposed model is validated by experiments on a real industrial manipulator.

A Benchmark Problem for Robust Feedback Control of a Flexible Manipulator

A benchmark problem for robust feedback control of a flexible manipulator is presented. The system to be controlled is a four-mass system subject to input saturation, nonlinear gear elasticity, model uncertainties, and load disturbances affecting both the motor and the arm. The system should be controlled by a discrete-time controller that optimizes performance for given robustness requirements. Four suggested solutions are presented, and even though the solutions are based on different design methods, they give comparable results.

Experimental comparison of observers for tool position estimation of industrial robots

This paper investigates methods for tool position estimation of industrial robots. It is assumed that the motor angular position and the tool acceleration are measured. The considered observers are different versions of the extended Kalman filter as well as a deterministic observer. A method for tuning the observers is suggested and the robustness of the methods is investigated. The observers are evaluated experimentally on a commercial industrial robot.

Nonlinear gray-box identification using local models applied to industrial robots

In this paper, we study the problem of estimating unknown parameters in nonlinear gray-box models that may be multivariable, nonlinear, unstable, and resonant at the same time. A straightforward use of time-domain predication-error methods for this type of problem easily ends up in a large and numerically stiff optimization problem. We therefore propose an identification procedure that uses intermediate local models that allow for data compression and a less complex optimization problem. The procedure is based on the estimation of the nonparametric frequency response function (FRF) in a number of operating points. The nonlinear gray-box model is linearized in the same operating points, resulting in parametric FRFs. The optimal parameters are finally obtained by minimizing the discrepancy between the nonparametric and parametric FRFs. The procedure is illustrated by estimating elasticity parameters in a six-axis industrial robot. Different parameter estimators are compared and experimental results show the usefulness of the proposed identification procedure. The weighted logarithmic least squares estimator achieves the best result and the identified model gives a good global description of the dynamics in the frequency range of interest for robot control.

A DAE approach to Feedforward Control of Flexible Manipulators

This work investigates feedforward control of elastic robot structures. A general serial link elastic robot model which can describe a modern industrial robot in a realistic way is presented. The feedforward control problem is discussed and a solution method for the inverse dynamics problem is proposed. This method involves solving a differential algebraic equation (DAE). A simulation example for an elastic two axis planar robot is also included and shows promising results.

Robust Control Of A Flexible Manipulator Arm : A Benchmark Problem

A control engineering benchmark problem with industrial relevance is presented. The process is a simulation model of a nonlinear four-mass system, which should be controlled by a discrete-time controller that optimizes performance for given robustness requirements. The control problem concerns only the so-called regulator problem.

ILC applied to a flexible two-link robot model using sensor-fusion-based estimates

Estimates from an extended Kalman filter (EKF) is used in an iterative learning control (ILC) algorithm applied to a realistic two-link robot model with flexible joints. The angles seen from the arm side of the joints (arm angles) are estimated by an EKF in two ways: 1) using measurements of angles seen from the motor side of the joints (motor angles), which normally are the only measurements available in commercial industrial robot systems, 2) using both motor-angle and tool-acceleration measurements. The estimates are then used in an ILC algorithm. The results show that the actual arm angles are clearly improved compared to when only motor angles are used in the ILC update, even though model errors are introduced.

Frequency-Domain Gray-Box Identification of Industrial Robots

This paper considers identification of unknown parameters in elastic dynamic models of industrial robots. Identifying such models is a challenging task since an industrial robot is a multivariable, ...

Modeling and Parameter Estimation of Robot Manipulators Using Extended Flexible Joint Models

This paper considers the problem of dynamic modeling and identification of robot manipulators with respect to their elasticities. The so-called flexible joint model, modeling only the torsional gea ...

Experimental Comparison of Methods for Multivariable Frequency Response Function Estimation

Nonparametric estimation methods for the multivariable frequency response function are experimentally evaluated using closed-loop data from an industrial robot. Three classical estimators (H1, join ...