The gold standard to study gait and balance is in the field but is challenging for those with impaired neuromuscular conditions. Using treadmills can be an alternative with some benefits including experiments in a safe and controllable environment. Most treadmill studies use a fixed speed that may not adequately represent a participant’s normal walking speed. Several studies have compared treadmill and overground walking with conflicting biomechanical outcomes. Self-paced (SP) treadmills were designed to allow participants to walk at their own comfortable gait speed. Many algorithms have been developed to design SP based on various data sources including ground reaction forces, markers, marker-less, and 3D depth cameras. In this novel controller design, we present a non-linear algorithm that implements a self-paced procedure integrated with instrumented treadmills [1]. The algorithm attempts to prevent the subject from reaching the front and back of the treadmill via minimal treadmill acceleration. Our algorithm uses real-time data from the center of mass relative to the front and back ends of the treadmill. The controller adjusts the treadmill’s belt speed via belt acceleration at every time step. Numerous studies (with over 410 participants from different populations) have been conducted using this method in the safe environment of a lab with instrumented treadmills. Moreover, we provided a simulation of the SP algorithm in different controlled conditions in MATLAB. The treadmill’s belt speed calculated by the simulated algorithm matched well with the experimental belt speed in a Gait Realtime Analysis Interactive Lab (GRAIL) system. The algorithm works in real-time with the other multi-sensory systems integrated with the treadmill via D-Flow software. This SP algorithm can be used in several biomechanical scenarios of gait and balance and is a step toward a more standardized algorithm in biomechanics across various instrumented treadmills with different populations.