Robotic Trajectory Tracking: Position- and Force-Control

This thesis employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control. In a first phase, the focus is put on the following of a freeform surface in a discontinuous manner. Next to resulting switching constraints, disturbances and uncertainties, the case of unknown robot models is addressed. In a second phase, once contact has been established between surface and end effector and the freeform path is followed, a desired force is applied. In order to react to changing circumstances, the manipulator needs to show the features of an intelligent agent, i.e. it needs to learn and adapt its behaviour based on a combination of a constant interaction with its environment and preprogramed goals or preferences. The robotic manipulator mimics the human behaviour based on bio-inspired algorithms. In this way it is taken advantage of the know-how and experience of human operators as their knowledge is translated in robot skills. A selection of promising concepts is explored, developed and combined to extend the application areas of robotic manipulators from monotonous, basic tasks in stiff environments to complex constrained processes. Conventional concepts (Sliding Mode Control, PID) are combined with bio-inspired learning (BELBIC, reinforcement based learning) for robust and adaptive control. Independence of robot parameters is guaranteed through approximated robot functions using a Neural Network with online update laws and model-free algorithms. The performance of the concepts is evaluated through simulations and experiments. In complex freeform trajectory tracking applications, excellent absolute mean position errors (<0.3 rad) are achieved. Position and torque control are combined in a parallel concept with minimized absolute mean torque errors (<0.1 Nm)

CILAP-Architecture for Simultaneous Position- and Force-Control in Constrained Manufacturing Tasks

This paper presents a parallel control concept for automated constrained manufacturing tasks, i.e. for simultaneous position- and force-control of industrial robotic manipulators. The manipulator’s interaction with its environment results in a constrained non-linear switched system. In combination with internal and external uncertainties and in the presence of friction, the stable system performance is impaired. The aim is to mimic a human worker’s behaviour encoded as lists of successive desired positions and forces obtained from the records of a human performing the considered task operating the lightweight robot arm in gravity compensation mode. The suggested parallel control concept combines a model-free position- and a model-free torque-controller. These separate controllers combine conventional PID- and PI-control with adaptive neuro-inspired algorithms. The latter use concepts of a reward-like incentive, a learning system and an actuator-inhibitor-interplay. The elements Conventional controller, Incentive, Learning system and Actuator-Preventer interaction form the CILAP-concept. The main contribution of this work is a biologically inspired parallel control architecture for simultaneous position- and force-control of continuous in contrast to discrete manufacturing tasks without having recourse to visual inputs. The proposed control-method is validated on a surface finishing process-simulation. It is shown that it outperforms a conventional combination of PID- and PI-controllers