Self-tuning fuzzy PID control of manipulator grasping force based on improved particle swarm optimization algorithm
To improve the stability of underactuated robotic arms in the sorting process of fragile parts,a fuzzy PID control algorithm is proposed to optimize their grasping performance by im-proving the particle swarm algorithm.Firstly,the characteristics of the underactuated robotic arm grasping force control system were analyzed and a specific strategy was proposed that com-bines particle swarm algorithm optimization algorithm with fuzzy PID grasping force control sys-tem.Secondly,methods such as dynamic inertia weights were introduced into particle swarm al-gorithm to improve its iteration speed and avoid falling into local optima.On this basis,the im-proved particle swarm optimization algorithm was used to optimize the relevant parameters of the fuzzy PID controller,achieving online self-tuning of fuzzy rule weights and quantization factors,and solving the problem of PID parameters unable to be dynamically adjusted.Finally,the meth-od was simulated and analyzed.The results show that the control algorithm designed in this pa-per can achieve stable grasping force within 0.8 s,with a steady-state error of less than 0.2％,and a disturbance tuning time of 0.262 s.The transient response speed,control accuracy,and stability of the system are significantly improved.
part sortinggrasping force controlimprove particle swarm optimization algorithmfuzzy PID controlunderactuated robotic arm
万方数据
维普
