An event-triggered linear time-varying model predictive control framework for physiologically meaningful bipedal walking

Abstract

Bipedal walking, as the most representative form of locomotion, is important for health and quality of human life. Model predictive control (MPC) has been widely employed for bipedal walking generation, but existing methods often struggle to simultaneously ensure stable tracking performance, physiological plausibility, and acceptable computational cost. To address this issue, an event-triggered linear time-varying model predictive control (ET-LTV-MPC) framework is proposed. Based on oscillator-generated desired trajectories, the control framework integrates a time-varying linearized model for future state prediction with a re-optimization mechanism triggered by foot contact, allowing timely re-optimization at hybrid state transitions while explicitly enforcing physiological torque constraints. Numerical simulations have been conducted using a compass model and compared with representative controllers. The results show that the ET-LTV-MPC achieves superior tracking accuracy while maintaining the joint torque within the physiological constraints. It also reduces the peak error caused by foot contact and provides a more favorable balance between tracking accuracy and computational cost than NMPC. Parametric analysis reveals that the optimal prediction window is approximately 0.2 s into the future, which is comparable to reported sensorimotor response latencies during human walking. These findings demonstrate that the proposed ET-LTV-MPC provides an accurate, computationally efficient and physiologically meaningful tool for generating bipedal walking.