The　trajectory　design　for　unmanned　aerial　vehicle　(UAV)-assisted　mobile　edge　computing　(MEC)　networks　has　become　a　hot　research　topic.　In　the　UAV-assisted　MEC　scenario,　the　UAV　is　required　to　frequently　adjust　its　flight　trajectory　due　to　dynamic　factors　such　as　time-varying　task　offloading　requirements,　user　mobility,　and　transmission　environment　variation.　In　this　paper,　with　consideration　of　the　constraint　induced　by　the　UAV　flight　dynamics,　the　dynamic　trajectory　design　challenge　within　the　blockchain-based　multi-UAV-assisted　MEC　framework　is　investigated.　An　intelligent　algorithm　that　integrates　multi-agent　deep　deterministic　policy　gradient　(MADDPG),　linear　quadratic　regulator　(LQR),　and　CVXPY　solver,　named　MADDPG-LC,　is　proposed　to　achieve　real-time　joint　optimization　of　dynamic　trajectory　control　and　resource　allocation　with　respect　to　minimizing　weighted　energy　consumption　and　delays.　Numerical　simulation　results　demonstrate　the　efficacy　of　the　proposed　MADDPG-LC　algorithm　in　addressing　the　UAV　flight　dynamics　constraint,　which　has　generally　overlooked　in　existing　works　　©　2024　IEEE.