Neural-Network-Based Nonlinear Model Predictive Control of Suspension Gravity Offload System

To address the underactuation issue induced by passive dampers in a suspension Gravity offload(SGO) system, this paper introduces the utilization of active control using Pneumatic Artificial Muscles(PAM) to transform it into a fully actuated physical system. However, due to the inherent non-linear characteristics such as flexibility and hysteresis in PAM, achieving precise force control poses challenges. Therefore, this paper proposes a Neural Network-based Nonlinear Model Predictive Control (NMPC) approach. We apply the proposed approach to the constant force control of the SGO system based on PAM. Simulation results demonstrate a marked improvement in control accuracy when compared to the feedforward PID control method.