In　order　to　make　the　walking　gait　of　biped　robot　more　human　like,　this　paper　takes　the　human　walking　data　as　the　expected　gait　of　robot,　and　uses　the　periodic　characteristics　of　gait,　proposes　a　gait　tracking　control　strategy　of　Biped　Robot　Based　on　adaptive　gait　switching　algorithm.　Firstly,　this　paper　establishes　the　complete　dynamic　models　of　left　leg　support　phase　(LSP)　and　right　leg　support　phase　(RSP)　based　on　Lagrange　method,　then　designs　the　corresponding　LQR　gait　tracking　control　strategy,　and　uses　the　adaptive　weighted　particle　swarm　algorithm　(A　WPSO)　to　obtain　the　optimal　controller　parameters.　Finally,　the　threshold　range　of　plantar　contact　force　in　two　periods　are　estimated　based　on　the　adaptive　mechanism,　and　the　occurrence　of　gait　switching　is　detected　according　to　the　defined　decision　rules,　thus　trigger　the　control　strategy　in　the　next　stage　to　realize　the　walking　tracking　control　of　biped　robot.　The　experimental　results　show　that　only　two　LQR　controllers　to　realize　the　accurate　tracking　of　the　desired　gait　of　the　biped　robot,　and　the　maximum　gait　speed　reaches　two　steps/s,　which　is　close　to　the　human　gait　speed.　Compared　with　other　methods,　the　gait　is　more　human　like.　　©　2022　IEEE.