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Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
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This study presents a design of an adaptive neuro-fuzzy controller for tractors’ tillage operations. Since the classical controllers allows plowing depth errors due to the variations of lands structure, the use of the combined neural networks and fuzzy logic methods decreases these errors. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which permits the generation of fuzzy rules to cancel the nonlinearity and disturbances on the implement. The design and simulations of the system, which consist of a hitch-implement mechanism, an electro-hydraulic actuator, and a neuro-fuzzy controller, are conducted in SolidWorks and MATLAB software. The performance of the proposed controller is analyzed and is contrasted with a Proportional Integral Derivative (PID) controller. The obtained results show that the neuro-fuzzy controller adapts perfectly to the dynamics of the system with rejection of disturbances.
Taiwan Association of Engineering and Technology Innovation
Title: Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
Description:
This study presents a design of an adaptive neuro-fuzzy controller for tractors’ tillage operations.
Since the classical controllers allows plowing depth errors due to the variations of lands structure, the use of the combined neural networks and fuzzy logic methods decreases these errors.
The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which permits the generation of fuzzy rules to cancel the nonlinearity and disturbances on the implement.
The design and simulations of the system, which consist of a hitch-implement mechanism, an electro-hydraulic actuator, and a neuro-fuzzy controller, are conducted in SolidWorks and MATLAB software.
The performance of the proposed controller is analyzed and is contrasted with a Proportional Integral Derivative (PID) controller.
The obtained results show that the neuro-fuzzy controller adapts perfectly to the dynamics of the system with rejection of disturbances.
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