Fast and stable composite learning via high-order optimization

Fast and stable adaptation is necessary to achieve stringent tracking performance specifications in the face of large system uncertainties. This work develops a novel fast adaption architecture based on a high-order optimization idea, where an approximated filter of weight is applied to smoothen and stabilize the estimation process. Larger learning rate can be selected to achieve fast adaption in that high-frequency uncertainties are attenuated. Moreover, composite learning combined with filtering regression and experience replay technique is utilized to further smoothen and accelerate the parameter estimation process. Given a nonlinear plant with multi-input multi-output strict-feedback structure, the proposed adaptive control is integrated into the backstepping framework. The uniformly bounded property of the tracking errors and the approximation errors is proven by Lyapunov theory. The superiority of the proposed method is demonstrated by comparative simulations.