A novel B-spline neural backstepping controller design with mean-value approximation and first-order filters is proposed for a class of uncertain multiple-input-multiple-output nonaffine nonlinear systems. By combining the proposed systematic backstepping design technique with B-spline neural network structure, one not only has the improved tracking performance but also reduces the computation time. Moreover, using the proposed control scheme, the problems of higher-order derivative and complexity explosion can be solved. According to the stability analysis, it is proven that the tracking errors can be made small by tuning adjustable parameters appropriately. Finally, simulation results are provided to confirm the effectiveness and applicability of the proposed control scheme.
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