Enhancing stability and safety: A novel multi-constraint model predictive control approach for forklift trajectory

Abstract

The advancements in intelligent manufacturing have made high-precision trajectory tracking technology crucial for improving the efficiency and safety of in-factory cargo transportation. This study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control (ESS-MPC) method. This approach includes a multi-constraint strategy for improved stability and safety. The kinematic model for a single front steering-wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. To ensure vehicle safety, the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS-MPC tracking. The optimisation constraints are also updated with the key kinematic and dynamic parameters of the forklift. The ESS-MPC method improved the position and pose accuracy and stability by 57.93%, 37.83%, and 57.51%, respectively, as demonstrated through experimental validation using simulation and real-world environments. This study provides significant support for the development of autonomous navigation systems for industrial forklifts.