Power-assisted wheelchairs (PAW) are efficient means of transportation for disabled persons. The resulting human-machine system includes several unknown parameters such as the mass of the user or ground adhesion. Moreover, the torque signals produced by the human are required to design a robust assistive strategy, but measuring them with torque sensors increases significantly the cost of the system. Therefore, we propose a robust observer-based assistive controller using a polytopic representation. The closed-loop control design is composed of two elements: a state feedback controller with the full state at the input, and an unknown input observer to estimate human torques and feed them into the obtained controller. The goal is to guarantee an imposed H∞ estimation performance while achieving reference tracking. To achieve the predefined performance, the observer gains are computed by solving an LMI problem. Finally, simulation results validate the control design. The methodology follows patent WO2015173094 issued in 2015 (Mohammad et al. 2015).