Research on a Variable Universe Control Method and the Performance of Large Sprayer Active Suspension Based on an Artificial Fish Swarm Algorithm–Back Propagation Fuzzy Neural Network

Abstract

In view of the typical requirements of large high-clearance sprayers, such as those operating in poor road conditions for farmland plant protection and at high operation speeds, reducing the vibration of sprayer suspension systems has become a research hotspot. In this study, the hydro-pneumatic suspension (HPS) of large high-clearance sprayers was taken as the object, and a variable universe T-S fuzzy controller with real vehicle vibration data as input was proposed to control suspension motion in real time. Different from traditional semi-active suspension, based on the characteristics of variable universe extension factors, a training method combining the artificial fish swarm algorithm and the back propagation algorithm was used to establish a fuzzy neural network controller with precise input to optimize the variable universe. Then, the time-domain and frequency-domain response characteristics of HPS were analyzed by simulating the special road conditions typical of farmland. Finally, the field performance of the sprayer equipped with the new controller was tested. The results show that the error rate of the AFSA-BP algorithm in training the FNN could be reduced to 3.9%, and compared with a passive suspension system, the T-S fuzzy controller improved the effects of spring mass acceleration, pitch angle acceleration, and roll angle acceleration by 18.3%, 23.3%, and 27.7%, respectively, verifying the effectiveness and engineering practicality of the active controller in this study.