Trajectory Tracking of AGV with Control Constraints Based on MPC and Optimal Control Algorithm

Trajectory tracking control technology for Automated Guided Vehicle (AGV) is an important part of intelligent logistics and industrial automation. However, existing linearization methods exhibit limited tracking accuracy when addressing AGV trajectory tracking problems with control constraints. To overcome this limitation, this paper proposes a scheme based on model predictive control (MPC) and optimal control algorithm. Unlike the traditional linearization method, the scheme preserves the nonlinear properties without linearization, introduces a non-quadratic performance index to simplify the handling of control constraints, applies optimal control algorithm to enable tracking of the reference trajectory as accurately as possible. Finally, the effectiveness of the proposed scheme is validated through numerical experiments.