Musculoskeletal　models　play　an　essential　role　in　ankle　rehabilitation　research.　The　majority　of　the　existing　models　have　established　the　relationship　between　EMG　and　joint　torque.　However,　EMG　signal　acquisition　requires　higher　clinical　conditions,　such　as　sensitivity　to　external　circumstances,　motion　artifacts　and　electrode　position.　To　solve　the　nonlinear　and　time-varying　nature　of　joint　movement,　a　Functional　Electrical　Stimulation　(FES)　model　was　proposed　in　this　study　to　simulate　the　whole　process　of　ankle　dorsiflexion.　The　model　is　combined　with　muscle　contraction　dynamics　based　on　Hill　model　and　ankle　inverse　dynamics　to　connect　FES　parameters,　torques,　and　ankle　angles.　In　addition,　the　extended　Kalman　filter　(EKF)　algorithm　was　applied　to　identify　the　unknown　parameters　of　the　model.　Model　validation　experiment　was　performed　by　acquiring　the　actual　data　of　healthy　volunteers.　Results　showed　that　the　root　mean　square　error　(RMSE)　and　normalized　root　mean　square　error　(NRMSE)　of　this　model　were　11.93%+/-　0.53%　and　1.39　degrees　+/-　0.26　degrees,　respectively,　which　means　it　can　effectively　predict　the　output　variation　of　ankle　joint　angle　while　changing　electrical　stimulation　parameters.　Therefore,　the　proposed　mode　is　essential　for　developing　closed-loop　feedback　control　of　electrical　stimulation　and　has　the　potential　to　help　patients　to　conduct　rehabilitation　training.