Controller of Fatigue Testing Machine for Aerospace Thermal Connections based on Improved NSGA-III Algorithm

For a fatigue testing machine, when the aerospace flexible thermal connection components are subjected to tension and compression testing on the machine, the actual vibration frequency and amplitude are lower than the setting value. In order to solve this problem, this paper proposes an optimized PID controller based on improved non-dominated sorting genetic algorithm (reference point-based non-dominated sorting genetic algorithm, NSGA-III), to promote the test speed and efficiency of the test system. First, the stability in the frequency domain is taken as the constraint condition, the overshoot, adjustment time and ITAE of the system are taken as the optimization targets, and the parameters Kp and Ki are used as the design variables to establish a multi-objective optimization model. Secondly, in view of the fixed rate of crossover and mutation operators used by NSGA-III algorithm, which is prone to problems such as premature convergence and poor search ability, an adaptive crossover and mutation operator improved non-dominated sorting genetic algorithm (NSGA-III) is proposed. Finally, MATLAB/Simulink conducts system simulation and compares the PID controller, NSGA-III optimized PID controller and improved NSGA-III optimized PID controller. The results show that the NSGA is improved when the input step response imposes disturbance. Compared with PID controller and NSGA-III optimized PID controller, the adjustment time of NSGA-III optimized PID controller is reduced by 0.21 s and 0.03 s, respectively, which shows that the improved NSGA-III optimized PID controller has stable position output and is more fast adjustment response and better anti-interference ability. Under the input sinusoidal response, the maximum position error of the improved NSGA-III is 0.05, which is 0.38 compared to the maximum position error of the PID controller, and the maximum position error of the NSGA-III optimized PID controller is 0.1, which reduces 86% and 50%, respectively. Improving NSGA-III to optimize the PID controller can significantly improve the dynamic tracking accuracy.