Learning approximation of feedforward control dependence on the task parameters with application to direct-drive manipulator tracking

This paper presents a new paradigm for model-free design of a trajectory tracking controller and its experimental implementation in control of a direct-drive manipulator. In accordance with the paradigm, a nonlinear approximation for the feedforward control is used. The input to the approximation scheme are task parameters that define the trajectory to be tracked. The initial data for the approximation is obtained by performing learning control iterations for a number of selected tasks. The paper develops and implements practical approaches to both the approximation and learning control. We propose a new learning control algorithm based on the online Levenberg-Marquardt minimization of a regularized tracking error index. The paper demonstrates an experimental application of the paradigm to trajectory tracking control of fast (1.25 s) motions of a direct-drive industrial robot AdeptOne. In our experiments, the learning control converges in five to six iterations for a given set of the task parameters.