Soft robot research has led to the development of platforms that should allow for better performance when working in uncertain or dynamic environments. The potential improvement in performance of these platforms ranges from mechanical robustness to high forces, to applying lower incidental contact forces in uncertain situations. However, the promise of these platforms is limited by the difficulty of controlling them. In this paper, we present preliminary results on simultaneously controlling stiffness and position for a pneumatically actuated soft robot. Improving on our prior work, we show that by including the pressure in our soft robot actuation chambers as state variables we can improve our average rise time by up to 137%, settling time by 119%, and overshoot by 853%. In addition to these improvements, we can now control both joint position and stiffness simultaneously. This performance improvement comes from using Model Predictive Control running at 300 Hz with improved dynamic models of the soft robot. High performance control of soft robot joints, such as the joint presented in this paper, will enable a wide range of robot applications that were previously difficult or impossible due to the rigid nature of traditional robot linkages and actuation schemes.