This　article　is　concerned　with　estimations　for　longitudinal　partial　linear　models　with　covariate　that　is　measured　with　error.　We　propose　a　generalized　empirical　likelihood　method　by　combining　correction　attenuation　and　quadratic　inference　functions.　The　method　takes　into　account　the　within-subject　correlation　without　involving　direct　estimation　of　nuisance　parameters　in　the　correlation　matrix.　We　define　a　generalized　empirical　likelihood-based　statistic　for　the　regression　coefficients　and　residual　adjusted　empirical　likelihood　for　the　baseline　function.　The　empirical　log-likelihood　ratios　are　proven　to　be　asymptotically　chi-squared,　and　the　corresponding　confidence　regions　are　then　constructed.　Compared　with　methods　based　on　normal　approximations,　the　generalized　empirical　likelihood　does　not　require　consistent　estimators　for　the　asymptotic　variance　and　bias.　Furthermore,　a　simulation　study　is　conducted　to　evaluate　the　performance　of　the　proposed　method.