An　intelligent　policy　iteration　tracking　control　method　is　proposed　for　the　tracking　control　problem　with　bounded　time-varying　disturbances.　An　adaptive　disturbance　compensator　is　designed　to　counteract　the　bounded　disturbance　and　guarantee　the　validity　of　the　Hamilton-Jacobi-Bellman　(HJB)　equation.　An　identifier　network　is　proposed　to　estimate　the　unknown　vehicle　dynamics,　and　a　new　HJB　equation　is　derived　using　the　reconstructed　identifier　tracking　error.　An　online　optimal　tracking　control　strategy　for　unmanned　vehicles　is　obtained　in　the　state　of　identifier　estimation　with　the　assistance　of　actor-critic　network.　Based　on　Lyapunov　theory,　it　is　demonstrated　that　the　identifier　tracking　error,　identifier　approximation　error　and　neural　network　weight　errors　are　all　semi-globally　uniformly　ultimately　bounded,　and　the　unmanned　vehicle　can　achieve　ideal　tracking　performance.　Simulation　results　indicate　that　with　a　disturbance　upper-bounded　by　10.331　1　N,　the　tracking　error　converges　to　at　least　0.054　8　m　which　exhibits　better　anti-interference　performance.　Compared　with　sliding　mode　control,　tracking　accuracy　is　improved　by　40%　and　control　cost　is　reduced　by　22%.　©　2025　Acta　Simulata　Systematica　Sinica.　All　rights　reserved.