Neural networks for nonlinear adaptive control

Abstract The paper deals with structures and learning methods for feedforward neural networks in the adaptive control of nonlinear systems. Nonlinear identification (including forward plant modelling, prediction modelling and inverse modelling) is discussed first, followed by consideration of direct and indirect MRAC and auto-tuning of PID controllers. Simulation results are reported in all cases.

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