面向食品自动化分拣的Delta机器人轨迹跟踪智能控制方法研究OA

[Objective]To address the limitations of existing trajectory tracking control methods for Delta robots in automated food production,such as insufficient tracking accuracy and delayed dynamic response,this study proposes an optimized control scheme to meet the high-precision and high-speed operational requirements of food sorting.[Methods]On the basis of an automated food production system,this paper proposes a trajectory tracking control method for Delta robots by integrating backstepping control,fractional order theory,sliding mode control,and adaptation law.Adaptation law provides real-time compensation for system parameter variations and external disturbances.Fractional order theory optimizes the dynamic response characteristics of the system,while backstepping control and sliding mode control cooperate to improve the stability of trajectory tracking.Additionally,a food sorting test platform is constructed to verify the performance of the proposed method.[Results]Compared with traditional trajectory control methods,the proposed method reduces the root-mean-square error of trajectory tracking by more than 50%and improves dynamic response speed by more than 8%.Meanwhile,it maintains high stability under varying load and disturbance conditions,effectively meeting the high-precision operation requirements of food sorting.[Conclusion]The integrated multi-method control strategy significantly improves both operational accuracy and response speed of Delta robots in automated food production.