Feedback linearization using neural networks
Automatica (Journal of IFAC)
Nonlinear Control Systems
Linear Optimal Control Systems
Linear Optimal Control Systems
IEEE Transactions on Neural Networks
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The paper proposes a unitary approach of adaptive output feedback control for non-affine uncertain systems, about which the positive knowledge refers to the relative degree r. Given a reference model, the objective is to design a controller that forces the measured system output to track the reference model output with bounded error. The components of the so called pseudocontrol, thought on a superposition effects principle, are the following: 1) the output of reference model, 2) the output of a Kalman type stabilizing compensator of the pair of systems composed by a) an output dynamics of a set of integrators of order tantamount to the assumed known relative degree r of the controlled system and b) an internal model, of order r - 1, oriented to the tracking error decreasing in the presence of step input signals, and 3) the adaptive control designed to approximately cancel the error of approximate dynamic inversion by virtue of whom the real control is hereby determined from pseudocontrol. A single hidden layer neural network is used to counteract this dynamic inversion error. The common approach of pseudocontrol design based on tracking error dynamics estimation is evaded. A proof of stable working of this intelligent type controller is sketched. The mathematical model for the longitudinal channel of a hovering VTOL-type aircraft is used as framework of synthesis and validation by numerical simulations.