Abstract
This paper presents a data-driven method to automatically tune gain-scheduled PI controllers for linear parameter-varying (LPV) systems. First, input-output data from a system is used to train a neural network (NN) based simplified additive nonlinear autoregressive exogenous (SANARX) model. After reformulating this into a state space representation, an H∞ method is used to obtain the PI parameters for any sampled working point. Throughout the entire pipeline, this work uses data from a real-world hydraulic test rig, which also serves as the system where the resulting gain-scheduled controller is evaluated on. Overall, no prior system knowledge is necessary to utilize this method.
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