Intention-Triggered Fuzzy Fixed-Time Control of Stochastic Euler–Lagrange Systems: Prosthesis-Human Cooperative Ambulation Method

The body's center of gravity (CoG) plays a pivotal role in human locomotion. Yet, regulating CoG for prosthesis human cooperative ambulation is often predefined, making it hard for the robot to sense real human intentions, particularly under stochastic disturbances. To address this, we propose an intention triggered fuzzy fixed-time (IT-FFT) control of stochastic Euler Lagrange systems. By using fuzzy identification, a data-driven intention model is proposed to continuously estimate the vertical range of CoG based on the walking velocity, as observed in locomotion data from ten participants. By integrating this intention model with an event-triggered model, we design a novel intention-triggered mechanism (ITM) that enables real-time intention recognition while conserving communication resources. Considering unknown initial states, we co-design the ITM and FFT scheme by utilizing fuzzy logic systems (FLSs), the backstepping technique, and stochastically fixed-time stability theory. The proposed IT-FFT controller guarantees system stability within a fixed time and ensures cooperative performance aligned with human intention. Applications show that our method reduces transmission by nearly 50% compared to time-triggered control, while also delivering superior cooperative performance over event-triggered control. This work paves the way for efficient control strategies in humanoid robots.