| The friction and wear phenomenon are widespread,and as aerospace,industrial manufacturing and other major industries continue to demand higher friction performance materials,the friction and wear tester is widely used as a powerful tool for investigating the strength,elasticity and other friction performance indicators of materials under given conditions.This thesis investigates the frictional properties of materials under variable load loading conditions using an electric variable load friction and wear tester.The simulation and experimental study of three control algorithms for tracking and control of the loading system in order to have a better control effect and dynamic performance for the problems of strong coupling,non-linearization and excess force interference of the electric variable load loading system.The details of the study are as follows:Firstly,the mechanical structure of the electric variable load friction and wear tester and the hardware and software components of the control system are given,the formulae for obtaining the friction force and friction coefficient during the loading process are derived,and the corresponding mathematical model is established for the working principle of the loading system.Secondly,a fractional-order PID controller is applied to the loading system,which has two additional parameters and a wider control range than conventional PID control.Stability analysis and approximation of the fractional-order operator are carried out for the control system,and the optimal controller parameters are obtained by an iterative search method using a particle swarm optimization algorithm with improved inertia weights ω.The effectiveness of the fractional-order PID controller is then verified by simulation and experiment.Then,considering the repetitive motion nature of the electric variable load loading system and the existence of non-linearization and inaccuracy of the mathematical model,an iterative learning controller is designed.It uses the system’s last control experience and deviation information to calculate its own next control input.The constraints and control flow of iterative learning are given and its convergence analysis is carried out,and the advantages of iterative learning control in reducing tracking errors and improving the tracking accuracy of the system are verified by comparing the simulation and experimental results with those of the PID control algorithm.Finally,in order to further investigate the control effect of the electric variable load loading system and improve the control performance of the controller,a fractional order iterative learning controller is designed and convergence analysis is carried out,considering the different advantages of fractional order calculus and iterative learning control respectively.The particle swarm optimization algorithm with improved inertia weights ω is used to adjust the control parameters of the controller.Simulations and experimental validation are carried out using different loading waveforms,and the results show that the controller has the advantages of strong anti-interference capability and smaller tracking error. |
