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Graduates Industrial Robotics:

Stig Moberg

Modeling and Control of Flexible Manipulators, PhD Thesis No 1349, Linköping University, Linköping, Sweden, 2010

Stig Moberg’s thesis, Modeling and Control of Flexible Manipulators, deals with different aspects of modeling and control of flexible, i.e., elastic, manipulators. For an accurate description of a modern industrial manipulator, it is shown that the traditional flexible joint model, described in literature, is not sufficient. An improved model where the elasticity is described by a number of localized multidimensional spring-damper pairs is therefore proposed. The main contributions of the work are the design and analysis of identification methods, and of inverse dynamics control methods, for the extended flexible joint model. A methodology to solve for the inverse dynamics based on the solution of a differential algebraic equation (DAE) is introduced. The inverse dynamics solution is then used for feedforward control of both a simulated manipulator and of a real robot manipulator. The last part of the work concerns feedback control. First, a model-based nonlinear feedback control (feedback linearization) is evaluated and compared to a model-based feedforward control algorithm. Finally, two benchmark problems for robust feedback control of a flexible manipulator are presented and some proposed solutions are analyzed.

Johanna Wallén

Estimation-based iterative learning control,
PhD Thesis No 1358, Linköping University, Linköping, Sweden, 2011.

Johanna Wallén’s thesis, Estimation-based iterative learning control, defended in February 2011, describes a method for self-learning with the aim to substantially improve the precision of robots. The main contribution of the thesis is to consider ILC algorithms applied to a flexible dynamic system where the controlled variable cannot be measured directly. The idea is to use indirect measurements, a model of the system, and estimation techniques to get a calculated value of the controlled variable. In the thesis Wallén also introduces estimation based Iterative learning control (ILC), a framework where it is possible to analyze stability and performance in the combination of estimation and ILC.  Experimental results are also included to show the usefulness of the concept and the framework for analysis and design.

Patrik Axelsson

Sensor Fusion and Control Applied to Industrial Manipulators
. PhD thesis, No. 1585, Linköping University, Linköping, Sweden, 2014.

Patrik Axelsson’s PhD thesis considers the fundamental question, how to control a flexible mechanical system when limited measurement are available from the controlled variable. Axelsson considers the general control problem in two parts. The first part is the estimation of the controlled variable based on measurements of the actuator position and the acceleration of the end-effector. Bayesian estimation methods for state estimation, represented by the extended Kalman filter (EKF) and the particle filter (PF), are evaluated and the methods are assessed in simulation as well as in experiments using an industrial manipulator and an industrial control platform. The second part of the work considers the control problem including the estimated values of the controlled variables in the controller. One approach which is proposed is the estimation-based norm-optimal ILC algorithm where the objective is extended to include not only the mean value of the estimated variable but also information about the uncertainty of the estimate. A second approach considered in the thesis is H-infinity control where a method for control of a flexible joint, with non-linear spring characteristics is proposed and evaluated in simulation.

André Carvalho Bittencourt

Modeling and Diagnosis of Friction and Wear in Industrial Robots, PhD thesis, No. 1617, Linköping University, Linköping, Sweden, 2014.

André Carvalho Bittencourt’s PhD thesis deals with robot fault diagnosis and monitoring of industrial robots. The main focus is on the design of methods for the detection of excessive degradations due to wear in a robot joint. A mathematical model of friction is developed, including dependence on temperature and load, in addition to the velocity dependence normally present in friction models. Based on a proposed friction model and friction data collected from dedicated experiments, a method is suggested to estimate wear-related effects to friction. A part of the work is also devoted to the development and evaluation of methods to estimate the wear directly. Finally a part of the work focuses on developing a method that can be applied without modifying the production cycles, i.e. to use data from normal use of the robot. A key feature is that the majority of the robots used in industry carry out their operation repeatedly. This has resulted in a method that is based on monitoring of the torque distribution. The key element is to compare the distribution from different repetitions of a cycle, using different statistical distance measures related to the distribution, and to diagnose wear changes based on the distance measure.

Graduates Aerial Vehicles and Marine Vessels:

Roger Larsson

System Identification of Flight Mechanical Characteristics
, Licentiate’s thesis No. 1599, Linköping University, Linköping, Sweden, 2013.

Roger Larsson is an industrial PhD student from Saab and his licentiate’s thesis concerns aircraft system identification. It covers two different areas that are highly relevant for the industrial application. The first topic concerns a sequential system identification method for online aircraft modeling. The idea is to use this method in Saab’s flight test monitoring system in order to provide the engineers with more reliable measures of the information content in the collected data. This method has a significant cost-saving potential and the process of implementing it in the real flight monitoring system has already been started. The second topic of the licentiate’s thesis concerns identification of unstable nonlinear systems operating under closed-loop conditions, like the Gripen aircraft produced by Saab. The project has focused on the development and evaluation of a number of candidate approaches to this challenging problem and benchmarked the different methods using both simulated and real flight test scenarios.

Zoran Sjanic

Navigation and Mapping for Aerial Vehicles Based on Inertial and Imaging Sensors. PhD thesis No 1533, Linköping University, Linköping, Sweden, 2013.

Zoran Sjanic’s PhD thesis concerns navigation and mapping for aerial vehicles based on inertial and imaging sensors. The main idea is to use sensor fusion of signals from relatively cheap sensors for navigation and surveillance purposes in UAVs. For example, by combining Synthetic Aperture Radar (SAR) data and data from inertial sensors, more accurate and focused SAR images as well as better estimates of the motion of the vehicle can be obtained. Furthermore, camera images and inertial sensors can be used to support the navigation system and simultaneously build a three-dimensional map of the observed environment. This is an example of inertial-visual Simultaneous Localization and Mapping (SLAM).

Martin Skoglund

Inertial Navigation and Mapping for Autonomous Vehicles. PhD thesis No 1623, Linköping University, Linköping, Sweden, 2014.

Martin Skoglund´s PhD thesis deals with inertial navigation and mapping for autonomous vehicles. One SLAM application in this thesis concerns determination of sensor and vessel positions in a scenario where a vessel with a known magnetic signature moves in a region covered by a network of magnetometer sensors. Another application concerns remotely operated underwater vehicles and the proposed method can be used to improve the navigation performance using a hydrodynamic model and data from the vessel’s sensors. The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. It is shown how a SLAM estimate can be improved by solving a nonlinear least-squares problem initialized using EKF-SLAM or by the use of an expectation maximization approach. Some benefits are the scalability of the approach as well as the accuracy of the results.