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Abstract

The variable universe fuzzy controller can realize the continuous control of the whole operation process of the independent metering control excavator, which significantly improves the operation performance and energy saving. However, VUFC fuzzy rules are designed according to expert experience and debugging many times. VUFC design process lacks of effective design methods, and has great randomness. The optimal state of VUFC fuzzy rules and relevant parameters such as universe cannot be ensured. Artificial neural network is a parallel distributed system, which overcomes the defects of traditional artificial intelligence based on logical symbols in processing intuitive and unstructured information. It also has the advantages of self-adaptation, self-organization and real-time learning. In this paper, the neural network learning algorithm is introduced into the fuzzy inference system to optimize the structure and parameters of the fuzzy controller. ANFIS is trained according to the input and output data of the system to obtain a fuzzy controller. Compared to traditional fuzzy controller, ANFIS can reduce the influence of subjective factors in the design process on the controller. Through the simulation model of 37-ton IMC excavator, the ANFIS controller is verified to have better control performance than VUFC. In the process of lifting and lowering the boom, the ANFIS controller has a more stable running speed, significantly reduces pressure and flow fluctuation amplitude.

Keywords

Independent metering control adaptive neuro-fuzzy inference system variable universe fuzzy controller excavator

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Optimization of VUFC for an IMC construction machinery based on ANFIS

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