Data-driven Modeling of Robotic Manipulators – Efficiency Aspects

Sammanfattning: Robotic manipulators are used for industrial automation and play an important role in manufacturing industry. Increasing performance requirements such as high operating speed and motion accuracy conflict with demands on heavy pay-loads and light-weight design with reduced structural stiffness. The motion control system is a key factor for dealing with these requirements, particularly for increasing the robot performance, improving safety and reducing power consumption. Most industrial robot control systems rely on current and angular position measurements from the motors, meaning that the actual controlled variable, that is the position of the robot’s end-effector, needs to be calculated using a model. Therefore, the mathematical model used for motion control must accurately describe the system’s dynamic behavior. Based on physics equations, the model contains unknown parameters that are usually identified from experimental data. This identification is a challenging problem, since the equations are nonlinear in the parameters, the system is highly resonant and experiments can only be done in closed loop with a controller. Assuming a real robot is available for experiments, data-driven identification is common in order to obtain the most accurate description of the real system’s behavior. The method applied in this thesis estimates the dynamic stiffness parameters by matching the model’s frequency response function to the system’s frequency response, which is obtained from measurements done with the closed-loop robot system. The main focus of this thesis are strategies for increasing the process efficiency such that the time it takes to do the experiments is reduced, while the quality of the model is maintained or improved. Two strategies related to experiment design are presented: First, the number of quasi-static robot configurations for data collection is decreased by choosing the most informative configurations from a set of candidates. Second, less data-demanding methods for estimating the system’s frequency response are considered. The effectiveness of the presented approaches is demonstrated both in simulation and with real data. If no robot is available for experiments, e.g. in the development phase, a model must be built based on specification data of components and other information available to the designer, such as CAD data. This thesis contains a modeling approach that derives a high-fidelity robot model of low order (lumped parameter model with few degrees of freedom) by combining results from test-rig measurements of isolated components with carefully reduced finite element models of the robot’s structural parts.