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Two MTNs are used to implement the accurate tracking control. First, to compensate for the influence of time-delay, MTN is taken as a predictor and the damped recursive least squares algorithm is used as its online learning algorithm. Second, a feed-forward MTN controller is developed on the basis of the proportional\u2013integral\u2013derivative controller, and the closed-loop errors between the reference input and the system output are directly chosen to be the MTN controller\u2019s inputs. The back propagation algorithm is introduced for its learning which can update its weights online at stable learning rate by the errors caused by the system\u2019s uncertain factors. Convergence and stability analysis are given to guarantee the performance of our proposed approach. 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