Although experimental designs have been extensively applied for steady-state modeling of process behavior, their use in studying dynamic systems has been more limited. Optimal designs were primarily focused on minimizing uncertainty in the transfer function parameter estimates, until Ljung pointed out the importance of minimizing bias in the assumed form of the transfer function. This paper reviews existing methodologies for designing input test signals and draws comparisons among them through the use of simulation examples. Results indicate that input designs which emphasize relevant frequencies for the controller application give superior performance when either an adequate process transfer function model has been specified or bias in its form exists. In the latter case, minimization of the frequency domain bias is found to be the most important factor in ensuring reliable controller performance. Distributional results for the closed-loop performance criterion provide important insights into the dominant source of model uncertainty.