The non-inverting buck-boost converter is suitable for battery powered applications as it can operate in buck, buck-boost, and boost modes. Traditional current mode and voltage mode controllers are generally employed to control it in closed loop. It is well established that a peak V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> controller provides superior load-transient performance compared to conventional current mode and voltage mode controllers. However, a peak V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> controller cannot be applied to the non-inverting buck-boost converter during boost and buck-boost modes because of its non-minimum phase behavior. This paper proposes a digital peak V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> controller which can work effectively during all the modes. The proposed controller samples both the inductor current and output voltage at the rate of the switching frequency. Thus, digital implementation of this controller does not need high-speed ADCs. Unlike traditional peak V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> controllers, the proposed controller does not depend on effective series resistance (ESR) of the output capacitor. An average model-based small-signal model of the proposed controller is presented. Analysis shows that a sufficient current loop gain can make the system behave like a first order system. This enables the designer to employ a PI controller to achieve superior transient performance in all the modes. Furthermore, fast-scale stability analysis of the controller is carried out using approximate discrete-time models to derive an analytical stability boundary of slope compensation. A non-inverting buck-boost converter prototype is fabricated and the proposed controller is implemented using an FPGA platform. Experimental results show close correlation with the analysis.