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Industrial robot control
Industrial robots are routinely used for pick and place operations, or for applications not requiring application of force or positioning accuracy exceeding 500 micrometers.
Their application can be further extended in machining applications, which are more challenging and require accuracy on the order of tens of micrometers, and improved compliance.
The stiffness of the robot arm which is related to inferior compliance is an order of magnitude smaller compared to convectional CNC equipment used in machining. One approach of improving it is to add compensator device at the robot arm’s end effector. Then, controlling the robot arm can be conducted jointly with controlling the compensator device to achieve superior compliance and to suppress vibrations.
Keywords: Iterative Learning Control, Model Predictive Control, Hierarchical control, Robotics
The aim of this project is to investigate approaches to control jointly the robot arm and the compensator, aiming for preparing the robot arm for machining applications. Possible approaches include model predictive control and its extensions with mismatch learning and hierarchical control implementation.
There is an available system and compensator, and you will be supported by domain experts in robotics and control. The project is expected to investigate several approaches to control of the robot arm with the aim of extending its performance (accuracy and speed). An existing model of the robot arm and its calibraiton are available for the student's use during the project. The student is expected to get familiar with the existing work in the group and develop the new model on top of the existing code base. The project will involve experimental work at Technopark with the support of robotics- and mechanical engineers.
The aim of this project is to investigate approaches to control jointly the robot arm and the compensator, aiming for preparing the robot arm for machining applications. Possible approaches include model predictive control and its extensions with mismatch learning and hierarchical control implementation. There is an available system and compensator, and you will be supported by domain experts in robotics and control. The project is expected to investigate several approaches to control of the robot arm with the aim of extending its performance (accuracy and speed). An existing model of the robot arm and its calibraiton are available for the student's use during the project. The student is expected to get familiar with the existing work in the group and develop the new model on top of the existing code base. The project will involve experimental work at Technopark with the support of robotics- and mechanical engineers.
- Investigation of predictive control methods
- Different ways to couple the controller of the compensator which is functioning on a faster time scale, and the robot arm control
- Study of computational efficiency of the proposed control approaches and optimization for computational efficiency.
- Work on the experimental test setup at the Technopark in collaboration with groups from D-MAVT.
- Investigation of predictive control methods - Different ways to couple the controller of the compensator which is functioning on a faster time scale, and the robot arm control - Study of computational efficiency of the proposed control approaches and optimization for computational efficiency. - Work on the experimental test setup at the Technopark in collaboration with groups from D-MAVT.
**Alisa Rupenyan ralisa@ethz.ch
Efe Balta ebalta@ethz.ch
We are looking for a highly motivated student with a background in control and modeling. Background in robotics could be beneficial.
Familiarity with numerical methods, optimization, iterative learning control (ILC), and model predictive control (MPC) is a plus.
- No specific experience with industrial robot systems is necessary;
- Some familiarity with Python and experiments design is helpful;
- Proficiency in English.
Please send your resume/CV (including lists of relevant publications/projects) and transcript of records in PDF format via email to ebalta@ethz.ch and rupenyan@inspire.ethz.ch.
**Alisa Rupenyan ralisa@ethz.ch Efe Balta ebalta@ethz.ch
We are looking for a highly motivated student with a background in control and modeling. Background in robotics could be beneficial. Familiarity with numerical methods, optimization, iterative learning control (ILC), and model predictive control (MPC) is a plus.
- No specific experience with industrial robot systems is necessary; - Some familiarity with Python and experiments design is helpful; - Proficiency in English.
Please send your resume/CV (including lists of relevant publications/projects) and transcript of records in PDF format via email to ebalta@ethz.ch and rupenyan@inspire.ethz.ch.