In　this　paper,　a　neural-network-based　equivalent-input-disturbance　(EID)　approach　is　developed　for　a　servo　control　system　to　improve　the　disturbance-rejection　performance,　where　the　radial　basis　function　neural　network　(RBFNN)　is　used　to　approximate　an　EID　of　unknown　and　mismatched　disturbances.　First,　by　treating　the　Luenberger　state　observer　as　an　ideal　dynamics　of　the　real　system,　the　comparative　output　between　the　two　systems　is　used　to　construct　the　RBFNN-based　EID　estimator.　Then,　by　exploiting　the　two-degree-of-freedom　property　of　the　EID　approach,　the　system　analysis　and　design　of　the　closed-loop　control　system　is　simplified　to　those　of　the　tracking　subsystem　and　the　disturbance-rejection　subsystem.　Further,　a　design　algorithm　together　with　some　guides　is　given.　Finally,　a　case　study　of　a　motor　driver　system　demonstrates　the　validity　of　the　developed　method.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.