This　work　reports　a　terrain　perception　and　stiffness　adaptive　control　scheme　with　no　vision.　This　strategy　can　enable　quadruped　robots　to　operate　in　a　stable　posture　in　unstructured　geographic　environments.　We　design　a　terrain　perception　controller　using　Kalman　filtering　observation　technology　to　accurately　estimate　the　robot＇s　speed,　position,　and　attitude　variables.　The　compensation　effect　of　estimations　can　provide　real-time　prediction　of　the　external　conditions,　suppressing　the　body＇s　high-frequency　jitter　in　fluctuating　terrain　and　improving　anti-interference　ability.　Yet,　a　stiffness　adaptive　controller　using　impedance　theory　decreases　the　actuator＇s　torque　deviation　and　improves　the　robot＇s　dynamic　stability　by　solving　the　optimal　stiffness　change　law.　The　Lyapunov　approach　proves　the　stability　of　the　system.　In　comparative　simulations,　the　proposed　method　is　evaluated　against　the　MIT　strategy.　Results　show　that　while　the　MIT　method　exhibits　rapid　convergence　and　effectively　reduces　initial　Euler　angle　fluctuations,　it　also　introduces　increased　oscillations　during　the　transient　phase.　Simulation　results　and　hardware　experiments　illustrate　that　the　proposed　method　effectively　enhances　the　robot＇s　terrain　adaptability　and　motion　smoothness.