Real　control　of　robot　is　realized　successfully　by　using　spring　to　imitate　the　lumbar　of　human-being　and　adopting　LQR　(linear　quadratic　regulator).　For　posture　control　of　the　flexible　two-wheeled　self-balancing　robot,　a　kind　of　discrete　Hopfield　network　way　based　on　associative　study　is　presented,　and　self-adaptive　and　self-organizing　posture　control　is　accomplished　in　a　manner　of　biological　control.　Firstly,　a　reasonable　energy　function　is　defined　for　the　nonlinear,　strong　coupling　and　flexible　robot　system.　Then,　a　Hopfield　neural　network　controller　suitable　for　this　dynamic　process　is　designed　based　on　the　dynamics　equation　of　the　flexible　robot,　and　its　convergence　is　analyzed.　Simulation　experiments　show　the　effectiveness　of　the　method　and　prove　the　stability　of　the　system.　The　rationality　and　availability　of　the　system　posture　controller　design　are　indicated　through　analyzing　the　experiment　results　in　detail.