Thanks a lot for the answers. Maybe I didn't give enough information about my problem, but I'm not yet allowed to post pictures and describing everything would have led to a short story.
@Roland was perfectly right it's not the optimizers task to care about the behaviour of the target function, but as I mentioned I assume the model to be fix.
@Ben Bolker's suggestion to limit the additive part of the function to positive values led to an unsatifying result.
What I didn't mention was that m1 to m10 are mean values of a data collection I recorded. I solved my problem by using the variance of the recorded series as weights during the fitting process.
y=c(m1,m2,m3,m4,m5,m6,m7,m8,m9,m10)
d=data.frame(seq(1, 10, 1),y=y)
vars = c(var(lt1$V1),var(lt2$V1),var(lt3$V1),var(lt4$V1),var(lt5$V1),var(lt6$V1),var(lt7$V1),var(lt8$V1),var(lt9$V1),var(lt10$V1))
weights = rep(max(vars),10)/vars
fitFun <- function(x, add, b0, b1) {b0 + (x+add)^b1}
m=nls(y~fitFun(x,add,intercept,power),d,weights=weights,start=list(intercept=1,power=3.5,add=2),trace=T)