Predictive and Anisotropic Control Design for Robot Motion under Stochastic Disturbances

The paper deals with the design and comparison of model-based predictive control and anisotropic control formulated for the motion control of industrial robots-manipulators. Stochastic disturbances, usually occurring and entering a control process, are taken into account in the design to attenuate their undesirable influences. The explanation refers a specific online control parameter tuning for predictive control and introduces a single-pass offline optimization for anisotropic control. The aim is to point out features of the proposed advanced approaches in transition situations.