Car-following　behavior　is　closely　related　to　the　longitudinal　control　of　the　vehicle,　affecting　the　safety　of　the　vehicle　and　traffic　flow　stability.　In　order　to　interact　with　the　preceding　vehicle,　the　target　vehicle　usually　collects　the　driving　data　of　the　preceding　vehicle.　However,　data　acquisition　devices　often　face　malfunctions　caused　by　various　unpredictable　disruptions,　resulting　in　missing　value　problems.　This　may　cause　the　target　vehicle　to　make　wrong　control　decisions.　Given　this　situation,　a　new　car-following(CF)　model　considering　missing　data　based　on　Transformer-Generative　Adversarial　Networks　(TransGAN)　is　proposed.　Firstly,　Transformer　Network　with　multi-head　attention　is　used　to　deeply　extract　the　potential　features　from　incomplete　vehicle　state　data,　which　can　filter　important　information　from　the　input　and　focus　on　these,　while　capturing　long　distance　dependencies.　Secondly,　a　Generative　Adversarial　Network　is　constructed.　The　Generator　generates　the　future　multi-step　control　states　of　the　target　vehicle　based　on　the　features　extracted　by　Transformer　Network.　The　Discriminator　with　a　fully　connected　network　is　applied　to　simultaneously　ensure　the　generation　accuracy.　Finally,　our　proposed　model　was　trained　and　tested　on　a　publicly　available　NGSIM　I-80　dataset.　Compared　with　other　existing　advanced　works,　our　model　can　fit　the　actual　control　states　of　the　target　vehicle　with　higher　accuracy　under　different　data　missing　rates　of　the　preceding　vehicle,　which　demonstrates　that　the　proposed　method　effectively　improves　the　robustness　of　vehicle　longitudinal　car-following　control　under　missing　data.　IEEE