Heavy-duty　vehicles　with　long　bodies,　a　large　number　of　axles　and　large　loads　are　subject　to　increasingly　high　requirements　for　precise　steering　technology　due　to　the　increasing　trend　toward　energy　conservation　and　intelligent　assisted　driving　as　well　as　variable　driving　conditions.　In　this　paper,　an　energy-efficient　open　circuit　variable-speed　pump-controlled　steering　system　(OPCEHSSS)　adapted　for　heavy　loads　is　used,　but　its　strong　flow　output　nonlinearity　and　system　nonlinear　dynamic　behavior　greatly　impede　the　steering　performance.　Therefore,　in　order　to　reduce　the　influence　of　the　flow　leakage　of　the　fixed-displacement　pump　on　the　system　and　to　ensure　that　the　flow　output　of　the　system　matches　the　control　model,　a　mapping　model　based　on　the　fitting　of　a　two-layer　neural　network　algorithm　with　a　dynamic　real-time　compensation　strategy　(FNC)　is　proposed.　In　addition,　considering　the　strong　robustness　of　the　system　even　under　parameter　uncertainty　and　unknown　disturbance,　a　complex　nonlinear　mathematical　model　is　established　based　on　OPCEHSSS　physical　characteristics,　and　a　dual-objective　control　strategy　of　steering　angle　and　pressure　based　on　sliding　mode　control　(SMC)　is　proposed.　However,　in　order　to　reduce　the　influence　of　high-order　switching　discontinuity　on　the　steering　and　ensure　the　fast　convergence　of　the　control　system,　a　fast　super　twisting　algorithm　(STA)　based　on　double　saturation　function　of　the　boundary　layer　is　proposed.　The　experimental　results　show　that　the　three　different　controllers　can　effectively　reduce　the　steering　angle　error　after　the　introduction　of　FNC.　And　in　the　case　of　a　single　axle　loaded　with　6　tons,　the　improved　new　FNC+STA　integrated　dual-objective　control　strategy　improves　the　accuracy　by　53.16%　compared　with　PID　and　40.67%　compared　with　SMC.　The　steady-state　error　is　maintained　within　0.9　degrees,　realizing　the　high-performance　steering　tracking　control　of　OPCEHSSS　for　heavy　vehicles.