We　consider　statistical　inference　for　partial　linear　additive　models　(PLAMs)　when　the　linear　covariates　are　measured　with　errors　and　distorted　by　unknown　functions　of　commonly　observable　confounding　variables.　A　semiparametric　profile　least　squares　estimation　procedure　is　proposed　to　estimate　unknown　parameter　under　unrestricted　and　restricted　conditions.　Asymptotic　properties　for　the　estimators　are　established.　To　test　a　hypothesis　on　the　parametric　components,　a　test　statistic　based　on　the　difference　between　the　residual　sums　of　squares　under　the　null　and　alternative　hypotheses　is　proposed,　and　we　further　show　that　its　limiting　distribution　is　a　weighted　sum　of　independent　standard　chi-squared　distributions.　A　bootstrap　procedure　is　further　proposed　to　calculate　critical　values.　Simulation　studies　are　conducted　to　demonstrate　the　performance　of　the　proposed　procedure　and　a　real　example　is　analyzed　for　an　illustration.　(C)　2015　Elsevier　B.V.　All　rights　reserved.