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.