The present thesis addresses the problem of motion control of flexible-joint robot manipulators using motor sensors only. The global objective is to guarantee tracking performance and robustness with respect to modeling uncertainties, together with safe human-robot interaction in a collaborative scenario where the robot and the human operator share the same workspace. The first objective of performance is achieved through the experimental identification of a flexible model of the system and the use of this model for the design of advanced control laws implemented in a cascade structure. Two complementary approaches, based either on predictive (Generalized Predictive Control, GPC) or Hinfinity control frameworks, are considered to design predictive and robust two degrees-of-freedom controllers. Experimental evaluation and analysis of the proposed strategies is provided. The second objective of safety is addressed by a novel algorithm for human-robot collision detection, without force sensors and in the presence of modeling uncertainties. In order to efficiently separate the dynamic effects of the collisions from the effects due to modeling errors, the proposed approach includes adaptive filtering and uses dynamic thresholds depending on the robot state. Experimental evaluation demonstrates a good detection sensitivity which is consistent with safety standards and recommendations for collaborative robotics.
