This thesis provides insight and novel tools for investigation into the neuromotor control of human standing balance. To maintain upright standing, the human body integrates sensory inputs and activates lower limb muscles in a coordinated manner. Balance control mechanisms are not well understood, largely due to the lack of experimental tools. Existing devices modify sensory information or mechanically disturb the body;​ however, both approaches can induce unnatural corrective responses. One approach that does not perturb normal control mechanisms is to allow humans to balance in an immersive physics simulator, but no appropriate tool has been available. Furthermore, control models that describe unperturbed (quiet) standing are typically evaluated in computer simulations and rarely experimentally tested by activating human muscles. This thesis presents two studies that seek to answer the questions:​ a) Can we engage humans in an immersive balancing task decoupled from their body mechanics? b) Which control models accurately characterize standing balance?

The first study validates the design of a novel robotic system that enables subjects to safely balance according to a programmable physical model. When programmed with a subject’s own body mechanics, results show that the torque‐angle relationship (load stiffness) is similar to that of normal standing, and that load stiffness increases, as expected, with increasing sway frequency. By providing decoupled control over balance physics, this system enables novel investigations into the neural mechanisms of human standing.

The second study evaluates proposed control models for quiet standing within a control loop that stimulates human muscle actuation. Two factors differentiate the models:​ activation type and delay‐ reducing prediction. All evaluated models successfully balance in the absence of natural muscle activation but increase corrective activity and mechanical effort relative to natural standing. Intermittent activation reduces stimulation energy but increases sway. Prediction reduces sway for the intermittent case only. To develop more accurate control models, future work is recommended to reduce sway during intermittent activation, reduce feedback gains, increase predictor compensation, and vary the setpoint angle.

This work contributes to the understanding of balance neurophysiology and may lead to improved control models for body movement in healthy and balance‐impaired individuals.