To address the issue of accumulated path tracking errors in wheeled robot headland turning,an adaptive speed-tracking improved model predictive control algorithm is proposed.Firstly,an adaptive speed prediction algorithm based on fuzzy logic is proposed to enhance tracking performance.Path tracking accuracy is evaluated across different lateral error and preview road curvatures,and a fuzzy rule table is formulated to describe the relationship between longitudinal speed,lateral error and road curvature.Secondly,based on the decoupling of the state equations in the model predictive control algorithm into lateral and longitudinal components,a sliding mode control algorithm is designed for speed tracking control.Finally,experiments are performed on a real vehicle platform using U-shaped and smooth-shaped headland turning paths.The results demonstrate a 28.9% improvement in path tracking accuracy and a 62.3% reduction in control cycle solving time compared to traditional model predictive control algorithms.
wheeled robotspeed predictionfuzzy control theorypath trackingmodel predictive control
万方数据
维普
