Rha Ron Hensen

Frequency domain identification of dynamic friction model parameters

This paper presents a frequency domain identification of dynamic model parameters for frictional presliding behavior. The identification procedure for the dynamic model parameters, i.e., (1) the stiffness and (2) the damping of the presliding phenomenon, is reduced from performing several dedicated experiments to one experiment where the system is excited with random noise and the frequency response function (FRF) of the phenomenon is measured. Time domain validation experiments on a servomechanism show accurate estimates of the dynamic model parameters for the linearized presliding behavior.

Brief Friction induced hunting limit cycles: A comparison between the LuGre and switch friction model

In this paper, friction induced limit cycles are predicted for a simple motion system consisting of a motor-driven inertia subjected to friction and a PID-controlled regulator task. The two friction models used, i.e., (i) the dynamic LuGre friction model and (ii) the static switch friction model, are compared with respect to the so-called hunting phenomenon. Analysis tools originating from the field of nonlinear dynamics will be used to investigate the friction induced limit cycles. For a varying controller gain, stable and unstable periodic solutions are computed numerically which, together with the stability analysis of the closed-loop equilibrium points, result in a bifurcation diagram. Bifurcation analysis for both friction models indicates the disappearance of the hunting behavior for controller gains larger than the gain corresponding to the cyclic fold bifurcation point.

Modeling and identification for high-performance robot control: an RRR-robotic arm case study

This paper explains a procedure for getting models of robot kinematics and dynamics that are appropriate for robot control design. The procedure consists of the following steps: 1) derivation of robot kinematic and dynamic models and establishing correctness of their structures; 2) experimental estimation of the model parameters; 3) model validation; and 4) identification of the remaining robot dynamics, not covered with the derived model. We give particular attention to the design of identification experiments and to online reconstruction of state coordinates, as these strongly influence the quality of the estimation process. The importance of correct friction modeling and the estimation of friction parameters are illuminated. The models of robot kinematics and dynamics can be used in model-based nonlinear control. The remaining dynamics cannot be ignored if high-performance robot operation with adequate robustness is required. The complete procedure is demonstrated for a direct-drive robotic arm with three rotational joints.

Friction induced hunting limit cycles: an event mapping approach

Studies the occurrence of limit cycles for a simple PID-controlled motion system subjected to different shapes of static frictional damping functions. The friction characteristics are compared with respect to the so-called hunting phenomenon. In particular, a classification with respect to the ability to predict both stable and unstable limit cycles is obtained. Furthermore, the local stability of the equilibrium points is discussed and attractor basins are given. To perform this analysis, the closed-loop dynamics are represented with an equivalent one-dimensional map representing a so-called event map.

Closed-form kinematic and dynamic models of an industrial-like RRR robot

Presents closed-form kinematic and dynamic models of a robot with three rotational degrees of freedom. The derivation of the models and estimation of their parameters are explained. Relevancy of the models is investigated with a writing task. Validation results, obtained by simulation and experiment, establish correctness of the models and illuminate their practical benefits.

Modeling and identification of an RRR-robot

A dynamic model of a robot with 3 rotational degrees of freedom is derived in closed form. A systematic procedure for estimation of model dynamic parameters is suggested. It consists of the following steps: (i) identification of friction model parameters for each joint; (ii) calculation of optimal exciting trajectories, required for estimation of the remaining dynamic model parameters; (iii) estimation of these parameters using a least-squares method. The estimated model satisfactory reconstructs experimental control signals, justifying its use in model-based nonlinear control.

High performance regulator control for mechanical systems subjected to friction

Several control strategies are compared with respect to their performance for regulator tasks on mechanical systems that exhibit friction. For this purpose a classic PID-controller combined with mass and frictional feedforward is compared to (i) a PID-controller combined with a model-based friction compensation using the dynamic LuGre friction model and (ii) a gain-scheduled optimal PD-controller based on a polytopic linear model (PLM). The latter consists of a feedforward part and an optimal nonlinear feedback part. The controllers are compared to the classic PID-controller by means of experiments on a rotating arm subjected to friction. The performance for three third order point to point setpoints shows that the gain-scheduled optimal PD-controller outperforms the other controllers with respect to settling time and maximal error after setpoint. The tracking performance is comparable for the LuGre-based controller and the classic PID-controller where the tracking performance of the gain-scheduled PD-controller is limited.