Neural inverse control of a rotary flexible link
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Abstract
This paper presents a data-driven inverse-model control scheme for a rotary flexible-link (RFL) system,
with (θ) denoting the base angular position and (α) the relative tip deflection. The plant is identified from experimental data and represented as a continuoustime fourth-order state-space model. On this basis, an inverse-model controller is designed and implemented using an artificial neural network (ANN) of the multilayer perceptron (MLP) type, trained on regressors composed of delayed states and inputs. Validation relies on quantitative error metrics, transientresponse analysis, and an indirect discrete-time BIBO (Bounded Input–Bounded Output) stability certification obtained by identifying an equivalent closed-loop linear model. Six MLP architectures are compared under three reference scenarios. The selected configuration achieves the best trade-off between (θ) tracking and (α) vibration attenuation, with bounded closedloop signals and competitive settling times. The work integrates, into a single workflow, data-driven identification of the RFL, systematic MLP architecture selection, and discrete-time BIBO stability analysis, providing a reproducible framework for designing and objectively comparing inverse-model neural controllers in subactuated flexible systems.
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