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This paper presents a hybrid controller of soft control techniques, adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL), and hard control technique, proportional-derivative (PD), for a five-finger robotic hand with 14-degrees-of-freedom (DoF). The ANFIS is used for inverse kinematics of three-link fingers and FL is used for tuning the PD parameters with 2 input layers (error and error...
This paper presents surface electromyographic (sEMG)-based, real-time Model Reference Adaptive Control (MRAC) strategy for a prosthetic hand prototype. The proposed design is capable of decoding the prerecorded sEMG signal as well as the sensory force feedback from the sensors to control the force of the prosthetic hand prototype using a PIC 32MX360F512L microcontroller. The input sEMG signal is preprocessed...
This paper presents a surface electromyographic (sEMG)-based, optimal control strategy for a prosthetic hand. System Identification (SI) is used to obtain the dynamic relation between the sEMG and the corresponding skeletal muscle force. The input sEMG signal is preprocessed using a Half-Gaussian filter and fed to a fusion-based Multiple Input Single Output (MISO) skeletal muscle force model. This...
A hybrid of soft control technique of adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL) and hard control technique of proportional-derivative (PD) for a five-fingered, smart prosthetic hand is presented. The ANFIS is used for inverse kinematics and FL is used for tuning the PD parameters with two input layers (error and error change) using 7 triangular membership functions and 49...
This paper presents an intelligent adaptive neurofuzzy inference system (ANFIS) based fuzzy Mamdani controller for a multifingered prosthetic hand. The objective of the controller is to move the finger joint angles along predetermined paths representing a grasping motion. The initiation of the grasping task is evaluated via EMG-entropy data, measured at the forearm of the prosthetic user. In addition...
This paper presents a hybrid of soft computing or control technique of adaptive neuro-fuzzy inference system (ANFIS) and hard computing or control technique of finite-time linear quadratic optimal control for the 14 degrees of freedom (DOFs), five-fingered smart prosthetic hand. In particular, ANFIS is used for inverse kinematics, and the optimal control is used for feedback linearized dynamics to...
This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The simulations of this...
Hard computing based optimization algorithms usually require a lot of computational resources and generally do not have the ability to arrive at the global optimum solution. Soft computing algorithms on the other hand negate these deficiencies, by allowing for reduced computational loads and the ability to find global optimal solutions, even for complex cost surfaces. This paper presents two numerical...
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