Bhm Björn Bukkems

Learning-based identification and iterative learning control of direct-drive robots

A combination of model-based and iterative learning control (ILC) is proposed as a method to achieve high-quality motion control of direct-drive robots in repetitive motion tasks. We include both model-based and learning components in the total control law, as their individual properties influence the performance of motion control. The model-based part of the controller compensates much of the nonlinear and coupled robot dynamics. A new procedure for estimating the parameters of the rigid body model, implemented in this part of the controller, is used. This procedure is based on a batch-adaptive control algorithm that estimates the model parameters online. Information about the dynamics not covered by the rigid body model, due to flexibilities, is acquired experimentally, by identification. The models of the flexibilities are used in the design of the iterative learning controllers for the individual joints. Use of the models facilitates quantitative prediction of performance improvement via ILC. The effectiveness of the combination of the model-based and the iterative learning controllers is demonstrated in experiments on a spatial serial direct-drive robot with revolute joints.

Online identification of a robot using batch adaptive control

Abstract A technique to identify parameters of a robot dynamic model is presented in this paper. It is based on a batch adaptive control algorithm that, using a model of the robot dynamics, realizes a repetitive robot trajectory. The tracking error decreases due to a feedforward control input generated from the dynamic model. This feedforward input is computed after adaptation of the model parameters at the end of each trial. As the algorithm is effective, even if the model parameters are all initially set to zero, it can be used to recover their physical values. For that purpose, an identification experiment is carried out during which the robot is excited persistently. The estimation technique admits an online implementation without a delay between trials and is quite appealing for use in practice. Its merits are experimentally demonstrated on a spatial direct-drive robotic manipulator with 3 rotational joints.

Tracking control for piecewise linear systems using an error space approach: A case-study in sheet control

This paper presents the design of tracking controllers for piecewise linear systems, with application to sheet control in a printer paper path. The approach that we will take is based upon an error space approach, which is derived from linear systems theory. We will show that due to the discontinuity in the piecewise linear system, the resulting model in error space consists of both flow conditions, describing the dynamics in each regime, and jump conditions, describing the error dynamics at the switching boundaries. Two types of controllers are proposed that result in either full or partial linearization of the closed-loop error dynamics. To show the effectiveness of the control design approach in practice, the sheet controllers are implemented on an experimental paper path setup.