Composite learning from model reference adaptive fuzzy control

Function approximation accuracy and computational cost are two major issues in approximation-based adaptive fuzzy control. In this paper, a model reference composite learning fuzzy control (MRCLFC) strategy is proposed for a class of affine nonlinear systems with functional uncertainties. In the MRCLFC, a modified modelling error that utilizes data recorded online is defined as the prediction error, a linear filter is applied to estimate the derivatives of plant states, and both the tracking error and the prediction error are exploited to update parametric estimates. It is proven that the closed-loop system achieves semiglobal practical exponential stability by an interval-excitation condition which is much weaker than a persistent-excitation condition. The proposed strategy can guarantee accurate function approximation under greatly reduced computational cost. The effectiveness of the proposed MRCLFC strategy has been verified by applying it to an control problem of aircraft wing rock.