Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic Excavator

Accurate control of excavator trajectory is the foundation for intelligent and unmanned development excavators. The operation process requires multiple actuators to cooperate complete response action. However, existing methods realize a single actuator can no longer meet practical demand. Based on this, hybrid adaptive quantum particle swarm optimization algorithm (HAQPSO) proposed tune proportional integral derivative (PID) controller parameters enhancing accuracy actuators. To increase randomization search speed avoid local convergence QPSO, QPSO combined with circle chaotic mapping, Gaussian mutation operators, adjustment factors, while linear transformation contraction-expansion coefficient (CE) improved dynamic mode. Through interface block, co-simulation platform load-sensitive system constructed, experiments compound actions are carried out. simulation results show that—compared ZN-PID, PSO-PID, QPSO-PID—the error boom by 26.59%, 32.95%, 9.44%, respectively, which proves high HAQPSO-PID in controlling