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20th World Congress of the International-Federation-of-Automatic-control (IFAC)

作者： Beckers, Thomas Umlauft, Jonas Hirche, Sandra Tech Univ Munich Dept Elect & Comp Engn Chair Informat Oriented Control ITR Munich Germany

Computed-torque control requires a very precise dynamical model of the robot for compensating the manipulator dynamics. This allows reduction of the controller's feedback gains resulting in disturbance attenuation and other advantages. Finding precise models for manipulators is often difficult with parametric approaches, e.g. in the presence of complex friction or flexible links. Therefore, we propose a novel computed-torque control law which consists of a PD feedback and a dynamic feed forward compensation part with Gaussian Processes. For this purpose, the nonparametric Gaussian Process regression infers the difference between an estimated and the true dynamics. In contrast to other approaches, we can guarantee that the tracking error is stochastically bounded. Furthermore, if the number of training points tends to infinity, the tracking error is asymptotically stable in the large. In simulation and with an experiment, we demonstrate the applicability of the proposed control law and that it outperforms classical computed-torque approaches in terms of tracking precision. (C) 2017, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Stochastic control Stability of nonlinear systems data-based control nonparametric methods Adaptive system and control robotic manipulators

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Stable Model-based control with Gaussian Process Regression for Robot Manipulators

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IFAC-PapersOnLine 2017年第1期50卷 3877-3884页

作者： Beckers T. Umlauft J. Hirche S. Chair of Information-oriented Control (ITR) Department of Electrical and Computer Engineering Technical University of Munich Germany

关键词： Adaptive system control data-based control nonparametric methods robotic manipulators Stability of nonlinear systems Stochastic control

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