This paper report a research investigation that proposes to replace the inversion set present in the traditional feedback linearization approach by an artificial neural network resulting in a hybrid composition approach with a neural network and an analytical term. The method is applied into a hydraulic actuator position system together with a friction compensation approach also built using neural networks. The control strategy used is based on a cascade methodology that consists of interpreting the hydraulic positioning system model as two interconnected subsystems: a mechanical subsystem driven by a hydraulic one. As experimental results have indicated a significant system behavior dependence on the oil temperature, its effects are also studied and the proposed method was improved by the inclusion of the oil temperature information as an input for the neural network functions. Experimental results show the effectiveness of the proposed controller and their advantages when compared with the traditional analytical schemes with feedback linearization approaches.

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