| Summary: | This work presents the basis for an expert system design for robot control. The
system is based on a new taxonomy for robot control. This taxonomy identifies the main issues in
the design and selection of robot controllers and categorizes and relates these issues. A clear
distinction is drawn between the selection of robot control architectures and the controller
algorithms that are utilized within these architectures. In both cases a wide variety of
approaches are examined and evaluated based on characteristics such as disturbance
rejection, effective stiffness, allowable planing task error, dynamic motion characteristics,
sensor information, hardware requirements and model accuracy. Based on these
characteristics the controller architectures and controller algorithms are categorized and
evaluated using these major criteria: reliability, complexity and robustness. This review and
categorization gives rise to a taxonomy based on desired task, imposed criteria, and the
distinctive characteristics of various controller architectures and controllers described in the
taxonomy. Based on this taxonomy an open expert system for selecting suitable control
architectures and algorithms is developed. The appropriateness of the rules embedded in
expert system's inference engine as well as the correctness of the decisions made by the
expert system is verified via simulations and experiments. Simulation results are obtained by
applying selected control architectures and controllers on a two-link manipulator both for
free robot motion and constrained robot motion. Experiments are performed both for
constrained and unconstrained motion on a five-axis industrial robot. This system is
designed for implementation on a new real-time open architecture controller system. This
system has potential applications for industrial robots that are currendy limited to either very
restrictive proprietary controllers, or complex and specialized controller designs. === Applied Science, Faculty of === Mechanical Engineering, Department of === Graduate
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