Composite model reference adaptive control under finite excitation with unstructured uncertainties

This paper presents an online parameter update algorithm in the context of composite model reference adaptive control based on intermittent signal holding to improve convergence properties of the parameters representing the unstructured uncertainties in the absence of persistent excitation. The present study extends the algorithm which was previously developed by considering only the structured uncertainties for which the basis functions are known a priori. The proposed extension utilises the Gaussian radial basis function neural network as the model for the uncertainty assuming appropriate placement of the local basis functions in the state space. A notable distinction from the case with full knowledge of the features constituting the linearly-parameterised uncertainty model is that the extended algorithm introduces a robustifying modification in the earlier phase of operation to deal with the inevitable learning residual.