Nonlinear postural control paradigm for larger perturbations in the presence of neural delays

Maintaining balance is an essential skill regulated by the central nervous system (CNS) that helps humans to function effectively. Developing a physiologically motivated computational model of a neural controller with good performance is a central component for a large range of potential applications, such as the development of therapeutic and assistive devices, diagnosis of balance disorders, and designing robotic control systems. In this paper, we characterize the biomechanics of postural control system by considering the musculoskeletal dynamics in the sagittal plane, proprioceptive feedback, and a neural controller. The model includes several physiological structures, such as the feedforward and feedback mechanism, sensory noise, and proprioceptive feedback delays. A high-gain observer (HGO)-based feedback linearization controller represents the CNS analog in the modeling paradigm. The HGO gives an estimation of delayed states and the feedback linearization control law generates the feedback torques at joints to execute postural recovery movements. The whole scheme is simulated in MATLAB/Simulink. The simulation results show that our proposed scheme is robust against larger perturbations, sensory noises, feedback delays and retains a strong disturbance rejection and trajectory tracking capability. Overall, these results demonstrate that the nonlinear system dynamics, the feedforward and feedback mechanism, and physiological latencies play a key role in shaping the motor control process.