In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing.
Smith, A. M. C., Yang, C., Ma, H., Culverhouse, P., Cangelosi, A., & Burdet, E. (2015). Novel hybrid adaptive controller for manipulation in complex perturbation environments. PLoS ONE, 10(6). https://doi.org/10.1371/journal.pone.0129281