My code is:
# Univariate model specifications
uspec1 <- ugarchspec(mean.model = list(armaOrder = c(2,2))) # super, DM, FM
uspec2 <- ugarchspec(mean.model = list(armaOrder = c(0,1))) # DM, EM, FM
uspec3 <- ugarchspec(mean.model = list(armaOrder = c(0,0))) # DM, EM, FM
uspec4 <- ugarchspec(mean.model = list(armaOrder = c(0,2))) # DM, EM
uspec5 <- ugarchspec(mean.model = list(armaOrder = c(1,1))) # DM, EM, FM
uspec6 <- ugarchspec(mean.model = list(armaOrder = c(1,0))) # DM, EM, FM
uspec7 <- ugarchspec(mean.model = list(armaOrder = c(2,0))) # EM, FM
uspec8 <- ugarchspec(mean.model = list(armaOrder = c(2,1))) # EM
uspec9 <- ugarchspec(mean.model = list(armaOrder = c(3,2))) # FM

# Multispec()
superDM.multispec <- multispec(c((replicate(2, uspec1)), (replicate(9, uspec2)), (replicate(7, uspec3)), uspec4, (replicate(2, uspec5)), (replicate(2,uspec6))))

# Estimating univariate GARCH models specified above using multifit command
superDM.multifit <- multifit(superDM.multispec, cbind_superDM)

# Specifying the ADCC model
superDM.adccspec <- dccspec(uspec = superDM.multispec, dccOrder = c(1,1), model = "aDCC", distribution = "mvnorm")

# Model estimation
superDM.adccfit <- dccfit(superDM.adccspec, data = cbind_superDM, fit.control = list(eval.se = TRUE), fit = superDM.multifit)

# Get model based time-varying covariance and correlation matrices
superDM.cov <- rcov(superDM.adccfit) # extracts the covariance matrix
superDM.cor <- rcor(superDM.adccfit) # extracts the correlation matrix