园艺电动旋耕机器人设计与试验OA

In response to the issues of equipment shortages,high pollution,excessive power consumption,and poor maneuverability in traditional tractor-tillage operations in facility horticulture,an energy-driven,purely electric self-propelled tillage robot was designed.Based on operational scenarios and agronomic requirements,the overall structural scheme was determined,and an isolated DCDC electrical system architecture,modular tillage units,and an electro-hydraulic lifting system were developed.Using a virtual prototype simulation approach,a tillage depth MAP chart considering the chassis pitch angle and tiller inclination was constructed,and a BP neural network PID controller was employed to regulate the electro-hydraulic lifting system for tillage depth.To enhance control performance,an improved fruit fly optimization algorithm incorporating adaptive step size,Gaussian random walk,and population concentration judgment value optimization strategies was proposed for the initial weight tuning of the BP neural network PID.The results indicated that the improved fruit fly algorithm reduced the number of iterations by 9 and decreased the objective function value by 6.14%compared with that of traditional methods.The BP neural network PID controller,tuned based on this algorithm,achieved a regulation time of only 0.31 s under step signal conditions,with no steady-state error or overshoot,and exhibited rapid response characteristics to sinusoidal and dynamic ramp signals.Field validation demonstrated that the robot achieved an average tillage speed of 2.81 km/h,with an average deviation of 0.211 2 m,a deviation rate of 0.524%,and a minimum turning radius of 458 mm,meeting the requirements for flexible maneuvering.Under tillage depth conditions of 50 mm,100 mm,and 150 mm,the stability coefficients were 89.51%,88.47%,and 92.85%,respectively,and the soil fragmentation rates reached 70.51%,75.07%,and 89.14%,all exceeding the operational standards of a soil fragmentation rate greater than 65%and tillage depth stability above 85%.The research findings can provide a theoretical basis for the design and precise control of facility tillage machinery.