Research On Nonlinear Control Method Of Flexible Filter Driving Mechanism

With the rapid development of industrial robots, automation equipment, aerospace and other engineering fields, a harsher standard for the accuracy, reliability and service life of the important equipment transmission system has been put forward. Aiming at the problems existing in the traditional driving mechanism, Wang Jiaxu, a professor from Chongqing University, invented the flexible filter driving mechanism, which is a kind of precision driving mechanism with high precision, high reliability, long life and light weight. But the existence of the elastic material in filter driving mechanism greatly increases the flexibility of the system, which makes the system shows strong nonlinearity, increasing the difficulty of control for the system. In this paper, the nonlinear dynamics model of flexible filtering driving mechanism was established, considering the effects of nonlinear stiffness, nonlinear friction and external unknown disturbance existed in the system. According to the characteristics of the established model, in order to reduce the influence of nonlinear factors on the system and improve the control precision of the system, the nonlinear control methods of the flexible filter driving mechanism were studied. Finally, the correctness and effectiveness of the control methods were verified by means of simulation and hardware in the loop simulation. The main contents of this paper are as follows:(1) The nonlinear dynamics model of flexible filtering driving mechanism was established, considering the effects of nonlinear stiffness, nonlinear friction and external unknown disturbance existed in the system. The stiffness of the filtering gear reducer is measured and the parameters of nonlinear stiffness curve were identified. LuGre friction model was introduced to describe the nonlinear friction characteristics of the system model and the static and dynamic parameters of this friction model were identified by experiments.(2) According to the characteristics of the dynamic model established above, a backstepping control method of the flexible filter driving mechanism was used to design the controller. A simulation analysis was carried out in the MATLAB/Simulink environment and the control effect of this method was proved. But backstepping control method needs higher model parameters and has the shortcoming of “explosion of complexity”, to solve those problems, an adaptive neural network dynamic surface control of the flexible filter driving mechanism was employed. In this method, RBF neural networks were used to approximate unmodeled characteristics of the system and compensate the errors of the model. Two controllers were designed for the system dynamics model which contains unmodeled characteristics and inaccurate parameters. Simulation results showed that the latter has higher precision than the former.(3) To test the actual effect of the designed nonlinear control algorithms, a based-on LabVIEW and Simulink hardware in the loop simulation scheme for flexible filtering driving mechanism using the existing equipment conditions of author's laboratory was proposed. The scheme use S-function to realize real-time simulation of Simulink and use LabVIEW SIT toolbox to realize data exchange of LabVIEW and Simulink, giving full play to their advantages. The experimental results prove the correctness and feasibility of the proposed nonlinear control methods.