A NEURAL NETWORK STRATEGY FOR LEARNING OF NONLINEARITIES TOWARD FEED-FORWARD CONTROL OF PRESSURE-COMPENSATED HYDRAULIC VALVES WITH A SIGNIFICANT DEAD ZONE

摘要

A velocity feed-forward-based strategy is an effective means for controlling a heavy-duty hydraulic manipulator; in particular, a typical valve-controlled hydraulic manipulator, to compensate for valve dead-zone and other hydraulic valve nonlineari-ties. Based on our previous work on the adaptive learning of valve velocity feed-forwards, manually labelling and identifying the dead-zones and the other nonlinearities in the velocity feedforward curves of pressure-compensated hydraulic valves can be avoided. Nevertheless, it may take two to three minutes or more per actuator to identify a pressure-compensated valve's highly nonlinear velocity feed-forward in real-time with an adaptive approach, which should be reduced for realistic applications. In this paper, inspired by brain signal analysis technologies, we propose a new method based on deep convolutional neural networks comparing with the previous method to significantly reduce this online learning process with the strong nonlinearities of pressure-compensated hydraulic valves. We present simulation results to demonstrate the effectiveness of the deep learning-based learning method compared to the previous results with an adaptive control-based learning.