## 1 Introduction  
The fundamental limitations imposed by one degree of freedom (DOF) is overcome by the 2-DOF control structure and one such control is a combination of feedforward/feedback control as shown in Figure 1. Robust feedback control theory, such as quantitative feedback theory (QFT) [1], is essentially a 2-DOF structure containing a feedback controller (\(G\)) and a prefilter (\(F\)). The feedback controller is designed to desensitize the closed loop against the unknown (bounded) disturbance, system uncertainty to certain level, and the prefilter element is designed to satisfy the tracking specification. The main objective in QFT design is to minimize the feedback cost (high-frequency controller gain) which relates the bandwidth and sensor noise amplification [1]. In general, the MIMO design methodologies are generalized as the sequential design method and the non-sequential one [2]. In the former design, an equivalent single-input single-output (SISO) system considered is updated after each successive design and the latter one is design independent. These methods convert the MIMO design problem into a set of SISO equivalent design problems which in-turn has their own advantages and drawbacks. Tracking, disturbance attenuation and stability stand as the core problems in control systems [3]. The tracking problem in QFT is solved in two ways [4]: (i) classical tracking specification, keeping the magnitude of the tracking response within two given bounds [4; 5], and (ii) tracking error specification (TES), establishing tolerances on the magnitude of the difference between certain reference models and the tracking transfer function [6; 7]. In a TES approach (also known as model matching), phase information is captured in the specification, which has potential benefits for certain applications and reduction of overdesign as compared to the classical approach. The tracking and disturbance rejection, performances can be improved by the feedforward controller without losing the robustness [8]. In general, the feedforward controllers are designed using the plant inversion technique [8; 9] An inversion-based feedforward controller strategy improves the tracking performance relative to a pure feedback control as discussed in Reference [10] and the references therein. A sufficient condition for tracking performance is given as the plant uncertainty should be smaller than the size of the nominal plant divided by its condition number. The design of feedforward controller for the tracking problem in the MIMO process is addressed in Reference [11]. Based on the analytical decoupling technique, the feedforward signals are determined and the multi-objective optimization problem is solved to find the transition time of each output subject to actuator constraint, but it is not extended to uncertain systems. In the context of QFT, the multivariable model matching problem is addressed in Reference [6; 7]. The drawbacks of these methods are over design, the prefilter design in Reference [7] is an approximation procedure and it may result in improper solutions. Recently, a novel MIMO QFT design methodology is proposed in Reference [4] to address both the model matching and the (uncertain) disturbance rejection problems. The approach derives the non-conservative controller bounds based on the quadratic inequalities which are computationally expensive for large number of uncertain parameters. This is due to the possible combination of pairing between the plants (in model matching problem) and/or between the plant and the disturbance (in disturbance rejection problem). The designer needs some experience for selecting the bound balancing coefficients and the tolerance tightening factors (the factors multiplying the tolerances) at each design frequency, so the [PERSON] et al. method [4] is not so simple to use. This motivates us to develop a simple and computationally efficient non-sequential design method. The approach we employed here is equivalent disturbance attenuation (EDA) concept.