Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique. (21st May 2020)

• Record Type: Journal Article
• Authors: Rossomando, Francisco G.; Serrano, Emanuel; Soria, Carlos M.; Scaglia, Gustavo
• Other Names: Guo Rongwei Guest Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-05-21
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/3240210
• Languages: English
• ISSNs: 1024-123X

Abstract

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 sin tg − 1 . function instead of tanh . . Besides, this function is an alternative to the use of tanh . 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 tanh . . The controller is designed using a saturated SMC (sliding mode controller) and a dynamic variations observer based on GRNN (general regression neural network). 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 and simplicity of this method.