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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/1434

Title: FORCE VELOCITY CONTROL WITH NEURAL NETWORK COMPENSATION FOR CONTOUR TRACKING WITH PNEUMATIC ACTUATION
Authors: Abu Mallouh, Mohammed

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Keywords: FORCE CONTROL
CONTOUR TRACKING
NEURAL NETWORK
PNEUMATIC
Issue Date: 17-Sep-2008
Series/Report no.: Canadian theses
Abstract: Control of the contact force between a robot manipulator and a workpiece is critical for successful execution of tasks where the robot’s end effector must perform a contact operation along the contour of a workpiece. Representative tasks include polishing, grinding and deburring. Considerable research has been conducted on force control with electric robots. By contrast, little research has been conducted on force control with pneumatic robots. The later has the potential to be considerably cheaper. However, the compressible nature of air as the working fluid and relatively high friction means pneumatic robots are more difficult to control. The subject of this thesis is the design and testing of a controller that regulates the normal contact force and tangential velocity of the end effector of a pneumatic gantry robot while tracking the contour of a planar workpiece. Both experimental and simulation results are presented. A PI Force Velocity (FV) controller for contour tracking was designed and tested experimentally. Three different workpiece edge geometries were studied: straight, inclined and curved. The tracking performance with the PI FV controller was comparable to the performance reported by other researchers with a similar controller implemented with an electric robot. This result confirms the potential of pneumatically actuated robots in force control applications. A system model was developed and validated in order to investigate the parameters that affect performance. A good match between experiment and simulation was achieved when the friction of the z-axis cylinder was modeled with a Displacement Dependent Friction Model (DDFM) instead of a Velocity Dependent Friction Model (VDFM). Subsequently, a DDFM based friction compensator was designed and tested. However, it was found that performance could not be improved even with perfect friction compensation, due to the effects of system lag. Two Neural Network (NN) compensators were designed to compensate for both the lag and friction in the system. Simulation results for straight and curved edges were used to examine the effectiveness of the NN compensators. The performance of the PI FV controller was found to improve significantly when a NN compensator was added. This result confirms the value of NN’s in control compensation for tracking applications with pneumatic actuation.
Description: Thesis (Ph.D, Mechanical and Materials Engineering) -- Queen's University, 2008-09-16 12:29:44.679
URI: http://hdl.handle.net/1974/1434
Appears in Collections:Queen's Theses & Dissertations
Mechanical and Materials Engineering Graduate Theses

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