Chris Ganseman

Optimal robot excitation and identification

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 the case of maximum-likelihood parameter estimation. In addition, the use of finite Fourier series allows calculation of the joint velocities and acceleration 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.

Experimental identification of robot dynamics for control

The paper discusses the experimental identification of dynamic robot models for their application in model based robot control, e.g., computed torque control. The accuracy of these controllers relies highly on the ability of the robot model to accurately predict the required actuator torques. The paper shows how this application reflects on the choices that have to be made in the different steps of the identification procedure, and consequently on the accuracy of the obtained model parameters and actuator torque prediction.

Generation of Periodic Trajectories for Optimal Robot Excitation

This paper describes the parameterization of robot excitation trajectories for optimal robot identification based on finite Fourier series. The coefficients of the Fourier series are optimized for minimal sensitivity of the identification to measurement disturbances, which is measured as the condition number of a regression matrix, taking into account motion constraints in joint and cartesian space. This approach allows obtaining small condition numbers with few coefficients for each joint, which simplifies the optimization problem significantly. The periodicity of the resulting trajectories and the fact that one has total control over theirfrequency content, are additional features of the presented parameterization approach. They allow further optimization of the excitation experiments through time domain data averaging and optimal selection of the excitation bandwidth, which both help the reduction of the disturbance level on the measurements, and therefore improve the identification accuracy.

An integrated friction model with improved presliding behaviour

Abstract This paper presents a new dynamical friction model which allows accurate modelling both in the sliding and the presliding regions. Transition between these two regions is accomplished without a switching function. The model incorporates a hysteresis function with local memory. This last aspect proves essential for modelling presliding friction that is encountered in real physical situations. The model as a whole can also handle the Stribeck effect and stick-slip behaviour as has been demonstrated by validation on a KUKA IR 361 robot. In this sense, this model can be considered as more complete in comparison with others found in literature.

Design of PID Robot Controllers Via VSS Approach

Abstract Sliding mode control could be applied to the control of the point-to-point displacements of robot manipulators. with sliding manifolds defined by simple nonlinear functions of position errors. This results in smooth displacements without requiring preliminary velocity programming. The control law consists of nonlinear functions of the position errors and velocities, multiplied by decoupled sign functions of the switching variables. Substituting sat functions with limits equal to the bounds on the control actions, and introducing proper integral actions for good positioning accuracy, leads to Variable Structure Control Systems equivalent to nonlinear PID like controllers. Performance of this control system is appraised by experiments on an industrial robot with revolute joints.