In　this　article,　an　asymmetric　integral　barrier　Lyapunov　function　(AIBLF)-based　control　scheme　is　proposed　for　human-robot　interaction　(HRI),　with　which　robot-aided　human-compliant　space-constrained　muscle　strength　training　can　be　achieved.　First,　an　admittance　model　is　exploited　to　generate　compliant　desired　trajectory　with　the　input　of　human-robot　interaction　torque.　Then,　on　the　basis　of　the　super-twisting　algorithm,　a　nonlinear　observer　is　built　to　estimate　and　further　compensate　for　the　lumped　disturbance　applied　to　the　robotic　driving　joint,　including　the　active　torque　from　human　subject,　the　robotic　model　uncertainty,　the　friction,　etc.　Finally,　an　AIBLF-based　controller　involving　nonlinear　observer　is　proposed　to　solve　the　trajectory　tracking　issues　in　addition　to　the　general　constraint　of　training　task　space,　in　which　the　AIBLF　strategy　is　utilized　to　establish　an　asymmetric-constrained　training　task　space　with　adjustable　boundary　effects.　This　approach　ensures　that　the　training　environment　is　tailored　to　accommodate　individual　needs　and　preferences,　promoting　a　safer　and　more　comfortable　training　experience.　The　convergence　of　all　states　and　stability　analysis　for　the　closed-loop　system　are　presented　via　the　Lyapunov　stability　theory.　The　effectiveness　of　the　proposed　control　scheme　is　verified　by　a　single-joint　muscle　strength　training　robot　in　various　experiments,　and　it　is　worth　noting　that　this　method　can　be　easily　extended　to　other　multijoint　robotic　systems　with　the　demand　of　human　compliance　and　space　constraint.