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

Francisco G. Rossomando, Emanuel Serrano, Carlos M. Soria and Gustavo Scaglia

Mathematical Problems in Engineering, 2020, vol. 2020, 1-14

Abstract:

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

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3240210

DOI: 10.1155/2020/3240210

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