Suspended constant force (SCF) control is a critical technology in suspended gravity offloading systems. However, inherent underactuation, unmodelled dynamics, and external disturbances can significantly degrade control performance and even compromise system stability. In this article, pneumatic artificial muscle (PAM) actuators are used as a replacement for traditional passive dampers to address the underactuation problem. Additionally, we propose a novel systematic robust output feedback model predictive control (ROFMPC) framework, which incorporates a radial basis function neural network (RBFNN)‐based model compensator, a Luenberger state estimator, and a tube model predictive controller. The RBFNN‐based model compensator compensates for unmodelled dynamics, while the Luenberger state estimator observes external disturbances. The model predictive controller then generates the optimal control sequence. Analytical results indicate that our designed SCF system encounters similar control challenges as those in antagonistic PAM (APAM). Therefore, sufficiently comprehensive numerical simulations and physical experiments are conducted on the APAM platform to verify the effectiveness of the proposed control framework. These results demonstrate that the proposed ROFMPC framework significantly improves force trajectory tracking performance for constant force control.

Simulating low- or zero-gravity environments on the ground is an important technology in the field of space exploration. Suspension-based gravity offloading (SGO) systems are commonly used ground-based platforms for simulating space environments. Nevertheless, the control of SGO systems presents significant challenges due to external disturbances and stringent response time requirements. This study proposes a predefined-time (PdT) observer-based PdT control framework to address the SGO system control problem. To begin, a pneumatic artificial muscle-based active unloading mechanism is introduced to address the inherent underactuation problem in the SGO system. Thereafter, a PdT disturbance observer is introduced to estimate the external disturbances acting on the SGO system. Subsequently, a type of PdT controller is investigated for the SGO system. The system, including the PdT disturbance observer and the PdT controller, is proven to be PdT stable in the Lyapunov sense. Finally, sufficient numerical simulations and physical experiments are conducted to verify the superiority and effectiveness of the proposed control method.