Simulation of Typical Truck Backing into Warehouse in Fuzzy Mathematics

In the application of fuzzy mathematics, truck reversing into a warehouse represents a classic simulation problem that fully demonstrates the practicality of fuzzy logic in complex control tasks. Traditional precise mathematical methods struggle to accurately describe the dynamic adjustments during truck reversing, while fuzzy control simulates human driver behavior through fuzzy rules and membership functions, achieving more natural control logic.

This simulation typically involves variables such as truck position, angle, and velocity. By fuzzifying inputs (like distance between truck and warehouse, current yaw angle, etc.) and combining with a fuzzy rule base derived from expert experience, the system infers reasonable steering and velocity adjustment strategies. The fuzzy inference output then undergoes defuzzification to convert into specific control signals, enabling gradual adjustment of the truck's posture until successful parking is achieved.

This process not only validates fuzzy control's adaptability to nonlinear, uncertain systems but also provides inspiration for applications like autonomous driving and intelligent robot path planning. Through simulation, one can observe optimization effects of fuzzy rules, such as adjusting rule weights or modifying membership function shapes to improve control stability and efficiency.