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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   sintg    −  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.

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