Content area
Industrial robots can cause servo system instability during operation due to friction between joints and changes in end loads, which results in jittering of the robotic arm. Therefore, this paper proposes a hybrid sparrow search algorithm (HSSA) method for PID parameter optimization. By studying the optimization characteristics of the genetic algorithm (GA) and sparrow search algorithm (SSA), the method combines the global optimization ability of GA and the local optimization ability of SSA, thus effectively reducing the risk of SSA falling into local optimum and improving the ability of SSA to find global optimization solutions. On the basis of the traditional PID control algorithm, HSSA is used to intelligently optimize the PID parameters so that it can better meet the nonlinear motion of the industrial robot servo system. It is proven through experiments that the HSSA in this paper, compared with GA, SSA, and traditional PID, has a maximum improvement of 73% in the step response time and a maximum improvement of more than 95% in the iterative optimization search speed. The experimental results show that the method has a good suppression effect on the jitter generated by industrial robots in motion, effectively improving the stability of the servo system, so this work greatly improves the stability and safety of industrial robots in operation.
Details
Proportional integral derivative;
Control theory;
Control algorithms;
Genetic algorithms;
Mathematical models;
Servocontrol;
Robot dynamics;
Robot arms;
Global optimization;
Neural networks;
Controllers;
Robots;
Search algorithms;
Local optimization;
Stability;
Systems stability;
Optimization algorithms;
Parameters;
Industrial production;
Step response;
Robot control;
Industrial robots
; Feng, Tingping 1
; Song, Changlin 1 ; Li, Junmin 1 ; Yang, Simon X 2
1 School of Mechanical Engineering, Xihua University, Chengdu 610039, China;
2 School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada;