Due　to　parametric　uncertainties,　unknown　nonlinearities,　and　dynamic　external　disturbances,　it　is　a　challenging　and　valuable　task　for　heavy　vehicle　electro-hydraulic　power　steering　systems　to　realize　high-precision　tracking　control.　To　cope　with　this　complex　nonlinear　tracking　control　problem,　the　integral　sliding　mode　control　is　an　extremely　potential　control　method,　which　has　strong　robustness　to　model　uncertainties　and　unknown　disturbances,　and　can　effectively　reduce　the　steady-state　error　in　tracking　control　process.　However,　the　inherent　chattering　phenomenon　of　integral　sliding　mode　control　seriously　affects　its　control　performance.　In　order　to　suppress　the　chattering　while　ensuring　robustness,　adaptive　fuzzy　technique　is　adopted　as　an　effective　auxiliary　means,　which　can　not　only　deal　with　the　inherent　chattering　problem　of　integral　sliding　mode　control　and　a　priori　knowledge　of　the　disturbance　upper　bound　in　controller　design　but　also　dynamically　adjust　the　parameters　in　the　fuzzy　rules.　Moreover,　the　designed　adaptive　fuzzy-integral　sliding　mode　control　scheme　still　needs　the　precise　mathematical　models　of　the　control　systems.　But　it　is　difficult　to　obtain　the　model　for　heavy　vehicle　electro-hydraulic　power　steering　systems　with　highly　complex　and　coupling　properties.　Therefore,　to　further　improve　the　method,　this　paper　presents　a　novel　adaptive　fuzzy-radial　basis　function　neural　network-integral　sliding　mode　control　method　for　the　complex　systems　to　achieve　timely　and　accurate　steering　angle　tracking　control.　In　addition　to　the　advantages　of　adaptive　fuzzy-integral　sliding　mode　control,　the　modified　controller　no　longer　requires　the　precise　mathematical　models　of　heavy　vehicle　electro-hydraulic　power　steering　systems　and　realizes　the　continuous　adaptive　updating　of　weights.　Finally,　the　effectiveness　and　superiority　of　the　proposed　control　scheme　is　illustrated　by　comparisons　and　extensive　simulations.