Nonlinear programming (NLP) is a field in mathematics that focuses on iteratively decreasing an arbitrary cost function. In this paper, a NLP approach based on extremum seeking control (ESC) is modified for the purpose of autonomously tuning the controller gains of robots. The effectiveness and flexibility of the approach is demonstrated through experimental results on a six degrees-of-freedom (DOF) industrial robot. Both single joint, and multiple joint tuning experiments were performed.
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