Abstract
Feedback linearisation is an approach applied to non-linear control design and has attracted a great deal of research interest in recent years. The common assumptions are that the full state is measurable, and that the system is exactly linearly parameterised and feedback linearisable (input/state or input/output). With few exceptions, the robustness issue is not addressed. In practical implementation of exactly linearising control laws, the chief drawback is that they are based on exact cancellation of non-linear terms. If there is any uncertainty in the knowledge of the non-linear functions, the cancellation is not exact and the resulting inputoutput equation is not linear. The aim of this paper discusses the use of Neural Network (NN)-based adaptive control to get asymptotically exact cancellation.
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