双舵轮自动导引车轨迹追踪控制算法

为提高双舵轮自动导引车(automated guided vehicle, AGV)轨迹跟踪控制性能,设计了基于模型预测控制的轨迹跟踪控制器。以双舵轮自动导引车为研究对象,首先建立自动导引车运动学模型,求得车体转弯半径,并重点分析双舵轮自动导引车转向优势;然后,对双舵轮导引车建立了基于模型预测控制的轨迹跟踪模型,并考虑车体运动性能指标及各类约束,最后通过仿真实验验证了模型预测控制算法用于双舵轮自动导引车的可行性,且分析了控制时域权重和预测时域长度两项模型参数对系统性能的影响;与基于比例-积分-微分轨迹跟踪控制器的自动导引车进行轨迹跟踪对比仿真实验。最终实验表明,采用该模型预测控制器可以满足各类约束条件,高效精准完成对连续大曲率目标路径的有效跟踪,同时具有较高的实时性和鲁棒性。

Trajectory Tracking Control Algorithm for Double-steering Automated Guided Vehicle

In order to improve the trajectory tracking performance of double-steering automated guided vehicle (AGV), a trajectory tracking controller based on model predictive control is designed. Taking the double-steering automatic guided vehicle as the research object, the kinematics model of the automatic guided vehicle was established firstly to obtain the turning radius and to analyze the advantages of the double-steering automated guided vehicle while turning. Then, taking the vehicle performance parameters and various constraints into consideration, a trajectory tracking model based on model predictive control was established. Finally, the simulation experiment validated that the model predictive control algorithm can be applied to double-steering automated guided vehicle, and the influence of control time domain weight and predicting time domain length on system performance were analyzed. Besides, the comparison simulation experiment of trajectory tracking was carried out with the automatic guided vehicle based on the proportional-integral-differential trajectory tracking controller. The final experiment result shows that the model predictive controller can meet various constraints and complete the tracking of target path with continuous large curvature more efficiently and accurately with high real-time property and robustness.