We propose simple tools for robust design of controllers for SISO linear systems with or without uncer- tainty. These tools are based on the generalization of frequency domain, where the uncertainty is defined by generalized Bode envelopes (GBE). The utilization of generalized Nyquist and Mikhailov theorems, which uses the number of GBE crossings with certain predefined horizontal lines, to deduce stability, allows to obtain stability as well as performance specifications simultaneously. The same design rules can be applied to continuous as well as discrete time systems. We provide examples to demonstrate our method of controller design for various control systems. The main problem of control theory is a design of an appropriate controller for a given plant with prescribed specifications. The controller should be robust enough to manage the errors in the model, noise and disturbances. Then, the designed controllers can be used in diverse applications ranging from automotive to process control. In the classical control design theory (e.g., Bode (1945)), the noise and uncertainties are not explicitly modeled, thus the phase and gain margins are used to make the system more robust. In the modern optimal control Kwakernaak & Sivan (1972), the specifications are converted to some performance criterion that should be minimized. In our approach, we avoid the need in gain and phase margins by exploring the poles and zeros clustering in the complex plane. If all the poles in the closed loop are clustered inside an arbitrary chosen bounded region G , we say that this system is G stable and all the specifications are met. To provide better stability margins we may choose the location of the region farther to the left from the imaginary axis (for continuous systems). By choosing the region inside the unit circle, we can use the same tools to design discrete controllers for discrete plants. Further development of the robust design went in a few directions described below. The parametric methods of robust control are described in Bhattacharyya et al. (1995), and Barmish (1993). In these methods, the structure of uncertainty is defined aforehand, and the design procedure is based on alge- braic and optimization methods. Another type which is based on H ¥ norm was proposed by Zames (1981). A loop shaping in frequency domain that is based on H ¥ theory was proposed by McFarlane et al. (1992). The methods of loop shaping try to consider the sensitivity at low and high frequencies in the design. A more recent approach is based on the linear matrix inequalities (LMI) (e.g., Iwasaki & Hara (2004)), but this approach uses state space representation and not a transfer function. Our aim is to fill the gap between the traditional classical control design theory, and more mathematically inclined sophisticated modern robust control theory. One of the earliest attempts to explicit plant uncertainty description was formulated in the quanti- tative feedback theory (QFT) by Horowitz (2001). In this theory, the fixed controller structure is not assumed, and the design is made by utilization of Nichols chart and conversion of the specifications to the frequency domain. Similarly to QFT, our method is very intuitive allowing a designer to achieve the desired performance and to see what trade-offs are necessary. In contrast to QFT, we use generalized