Physiologically Consistent Postural Control via EHGO-Augmented Nonlinear Compensation

Accurate modeling and control of human postural stability remain central challenges in biomechanics, robotics, and rehabilitation engineering. Traditional approaches based on High-Gain Observers (HGOs) enhance state estimation but are highly sensitive to sensor noise, limiting their reliability in practical scenarios. This paper presents an Extended High-Gain Observer (EHGO)-assisted feedback linearization controller for a single inverted pendulum (SIP) model of human standing posture subject to actuator noise. By explicitly incorporating noise as an augmented state, the EHGO achieves robust state estimation while ensuring disturbance compensation. The controller simulates central nervous system (CNS) regulation by shaping nonlinear dynamics into a double-integrator form regulated via Linear Quadratic Regulator (LQR). Simulation results under three perturbation magnitudes (1.27, 1.37, and 1.47 rad) demonstrate fast recovery (<1.7 s), minimal overshoot, negligible steady-state error, and smooth torque transients free of high-frequency chatter, even in noisy conditions. Compared with existing observer-based approaches such as Linear Extended State Observer–Sliding Mode Control (LESO-SMC) exoskeleton control, the proposed design provides equally strong disturbance rejection while preserving continuous, physiologically consistent torque outputs. These results confirm that the SIP model, when augmented with EHGO-based control, remains a valid and powerful framework for analyzing postural stability and developing robust, human-inspired controllers for assistive and rehabilitation technologies.