Path Following Control of Automated Guided Vehicle Based on Model Predictive Control with State Classification Model and Smooth Transition Strategy

Path following control of the automatic guided vehicle (AGV) for material handling in the plant requires a smooth and high-precision continuous attitude adjustment. However, there are many difficulties in designing the controller, such as actuator saturation, parameter selection, and the influence of deviations relations. In this paper, an improved model predictive control SCMPC-ST with a state classification model (SCM) and a smooth transition (ST) strategy is proposed to address these problems. First, based on the deviations relations and the actuator saturation, the SCM is designed to divide the pose states of AGV into three stages. With clearly objective functions and boundary constraints, SCM allows the pose states can be transferred sequentially, which avoids the problem of parameter selection. Second, use the analytical method to resolve SCM to directly obtain the desired control law, which provides excellent performance in real-time. Finally, the smooth transition strategy is built to adjust the control law at the transition step and overshoot step to ensure the stability of the control process. Simulation results show that by focusing on adjusting the deviations relations, SCMPC-ST can make AGV eliminate all deviations continuously and smoothly while maintaining high accuracy.