Hierarchical control of complex manufacturing processes

The need for changing the control objective during the process has been reported in many systems in manufacturing, robotics, etc. However, not many works have been devoted to systematically investigating the proper strategies for these types of problems. In this dissertation, two approaches to such problems have been suggested for fast varying systems. The first approach, addresses problems where some of the objectives are statically related to the states of the systems. Hierarchical Optimal Control was proposed to simplify the nonlinearity caused by adding the statically related objectives into control problem. The proposed method was implemented for contour-position control of motion systems as well as force-position control of end milling processes. It was shown for a motion control system, when contour tracking is important, the controller can reduce the contour error even when the axial control signals are saturating. Also, for end milling processes it was shown that during machining sharp edges where, excessive cutting forces can cause tool breakage, by using the proposed controller, force can be bounded without sacrificing the position tracking performance. The second approach that was proposed (Hierarchical Model Predictive Control), addressed the problems where all the objectives are dynamically related. In this method neural network approximation methods were used to convert a nonlinear optimization problem into an explicit form which is feasible for real time implementation. This method was implemented for force-velocity control of ram based freeform extrusion fabrication of ceramics. Excellent extrusion results were achieved with the proposed method showing excellent performance for different changes in control objective during the process --Abstract, page iv

Iterative learning hybrid force/velocity control for contour tracking

In this paper, we propose a new method, which is based on an iterative-learning-control (ILC) algorithm, for the contour tracking of an object of unknown shape performed by an industrial robot manipulator. In particular, we consider (both implicit and explicit) hybrid force/velocity control whose performance is improved by repeating the task. Here, a time-based reference signal is not present, and therefore, a new approach has been developed, which is different from the typical applications of ILC. Experimental results show the effectiveness of the technique

Design of a robotic transcranial magnetic stimulation system

Transcranial Magnetic Stimulation (TMS) is an excellent and non-invasive technique for studying the human brain. Accurate placement of the magnetic coil is required by this technique in order to induce a specific cortical activity. Currently, the coil is manually held in most of stimulation procedures, which does not achieve the precise clinical evaluation of the procedure. This thesis proposes a robotic TMS system to resolve these problems as a robot has excellent locating and holding capabilities. The proposed system can track in real-time the subject’s head position and simultaneously maintain a constant contact force between the coil and the subject’s head so that it does not need to be restrained and thus ensure the accuracy of the stimulation result. Requirements for the robotic TMS system are proposed initially base on analysis of a serial of TMS experiments on real subjects. Both hardware and software design are addressed according to these requirements in this thesis. An optical tracking system is used in the system for guiding and tracking the motion of the robot and inadvertent small movements of the subject’s head. Two methods of coordinate system registration are developed base on DH and Tsai-lenz’s method, and it is found that DH method has an improved accuracy (RMS error is 0.55mm). In addition, the contact force is controlled using a Force/Torque sensor; and a combined position and force tracking controller is applied in the system. This combined controller incorporates the position tracking and conventional gain scheduling force control algorithms to monitor both position and force in real-time. These algorithms are verified through a series of experiments. And it is found that the maximum position and force error are 3mm and 5N respectively when the subject moves at a speed of 20mm/s. Although the performance still needs to be improved to achieve a better system, the robotic system has shown the significant advantage compared with the manual TMS system. Keywords—Transcranial Magnetic Stimulation, Robot arm, Medical system, Calibration, TrackingEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Design of a robotic transcranial magnetic stimulation system

Transcranial Magnetic Stimulation (TMS) is an excellent and non-invasive technique for studying the human brain. Accurate placement of the magnetic coil is required by this technique in order to induce a specific cortical activity. Currently, the coil is manually held in most of stimulation procedures, which does not achieve the precise clinical evaluation of the procedure. This thesis proposes a robotic TMS system to resolve these problems as a robot has excellent locating and holding capabilities. The proposed system can track in real-time the subject’s head position and simultaneously maintain a constant contact force between the coil and the subject’s head so that it does not need to be restrained and thus ensure the accuracy of the stimulation result. Requirements for the robotic TMS system are proposed initially base on analysis of a serial of TMS experiments on real subjects. Both hardware and software design are addressed according to these requirements in this thesis. An optical tracking system is used in the system for guiding and tracking the motion of the robot and inadvertent small movements of the subject’s head. Two methods of coordinate system registration are developed base on DH and Tsai-lenz’s method, and it is found that DH method has an improved accuracy (RMS error is 0.55mm). In addition, the contact force is controlled using a Force/Torque sensor; and a combined position and force tracking controller is applied in the system. This combined controller incorporates the position tracking and conventional gain scheduling force control algorithms to monitor both position and force in real-time. These algorithms are verified through a series of experiments. And it is found that the maximum position and force error are 3mm and 5N respectively when the subject moves at a speed of 20mm/s. Although the performance still needs to be improved to achieve a better system, the robotic system has shown the significant advantage compared with the manual TMS system. Keywords—Transcranial Magnetic Stimulation, Robot arm, Medical system, Calibration, TrackingEThOS - Electronic Theses Online ServiceGBUnited Kingdo