Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique

ROSSOMANDO, FRANCISCO G.; SERRANO, EMANUEL; SORIA, CARLOS M.; SCAGLIA, GUSTAVO

MATHEMATICAL PROBLEMS IN ENGINEERING

HINDAWI PUBLISHING CORPORATION

Lugar: New York; Año: 2020 vol. 2020 p. 1 - 14

1024-123X

This work presents a novel controller for the dynamics of robots using a dynamic variations observer. The proposed controlleruses a saturated control law based on sin(tg − 1 (.)) function instead of tanh(.). Besides, this function is an alternative to the useof tanh(.) in saturation control, since it reaches its maximum value more gradually than the hyperbolic tangent function. Usingthis characteristic, the transition between states is smoother, with similar accuracy to tanh(.). The controller is designed using asaturated SMC (sliding mode controller) and a dynamic variations observer based on GRNN (general regression neuralnetwork). The originality of this work is the use of a combination of adaptive GRNN with a sliding mode controller (SMC)including a new saturation function. Finally, experiments based on trajectory tracking demonstrate the robustness andsimplicity of this method.