Abstract
Model-based robot control algorithms require the on-line evaluation of robot dynamics, leading to hybrid continuous/discrete-time implementations. The performance of these fixed-gain control algorithms varies in the workspace and it is not adequate for trajectory-tracking. In this paper, we present a coherent discrete-time framework for the analysis of model-based algorithms and introduce predictors to compensate for modeling and discretization errors. The basic controller structure is not altered; an added supervisory module is proposed to monitor performance and adjust the command signal accordingly. The module injects a degree of adaptiveness in the controller and reduces the sensitivity of the design to unmodeled dynamics. Our preliminary simulation experiments confirm that one-step-ahead predictors lead to a more uniform performance and are suitable for trajectory-tracking applications.
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