Discrete adaptive neural network disturbance feedforward compensation for nonlinear disturbances in servo-control applications

This paper introduces a novel adaptive neural network compensator for feedforward compensation of external disturbances affecting a closed loop system. The neural network scheme is posed so that a nonlinear disturbance model estimate for a measurable disturbance can be adapted for rejection of the disturbance affecting a closed loop system. The nonlinear neural network approach has been particularly developed for ‘mobile’ applications where the adaptation algorithm has to remain simple. For that reason, the theoretical framework justifies a very simple least-mean-square approach suggested in a mobile hard disk drive context. This approach is generalized to a nonlinear adaptive neural network (NN) compensation scheme. In addition, usual assumptions are relaxed, so that it is sufficient to model the disturbance model as a stable nonlinear system avoiding strictly positive real assumptions. The output of the estimated disturbance model is assumed to be matched to the compensation signal for effectiveness, although for stability this is not necessary. Practical and simulation examples show different features of the adaptation algorithm. In a realistic hard disk drive simulation and a practical application, it is shown that a nonlinear adaptive compensation scheme is required for nonlinear disturbance compensation providing better performance at similar computational effort in comparison to well established schemes.