Integrated guidance and control based on nonlinear model predictive control with simplified primal–dual neural network optimization

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In this study, an integrated guidance control (IGC) approach based on nonlinear model predictive control (NMPC) is proposed for the problem of missile interception. It needs to be emphasized that, this approach does not require information of the target’s acceleration. In order to achieve missile interception, an NMPC problem is obtained by choosing a suitable cost function with the physical constraints of the missile into account. Then the NMPC problem is transformed into a quadratic programming (QP) problem. The QP problem can then be converted into a solution problem for linear variational inequalities (LVIs), which is further solved by a simplified primal–dual neural network (SPDNN). The efficacy and robustness of the proposed control methodology are demonstrated by numerical simulation.