The advanced Injection Molding Machine (IMM) control system is urgently needed because there is an increasing gap of control technology between domestic and overseas IMM controllers. In the paper, robust and adaptive designs of Iterative Learning Control (ILC) are investigated, and the related algorithms are implemented into injection velocity control and clamping mechanism’s position control. Firstly, the dissertation describes the current status and the developments of domestic IMM market. Then the IMM control technologies are surveyed briefly. Especially, the ILC algorithm is introduced and surveyed. Secondly, the systems analysis of robust and monotonically-convergent ILC is studied. Then several approaches that have been proposed in the literature to add convergence robustness to ILC are presented. Especially, the discrete Filtering ILC (FILC) is fully studied in the time and frequency domains. A novel adaptive update rule ILC algorithm and an improved time-frequency adaptive filter ILC algorithm are proposed. Thirdly, an adaptive friction compensation approach based on iterative learning is developed to address a class of nonlinear servo systems with repeatable actions and time variant friction model uncertainties, which are iteration independent. The simulation illustrations are given to show the approach can achieve high-accuracy trajectory control. Fourthly, the series products of real-time IMM control system are developed. Then, the hardware and software setup to test IMM are introduced. Fifthly, the adaptive update rule ILC is applied to improve the clamping mechanism’s position precision of IMM. Furthermore, a changeable update rule is introduced to accelerate the convergence speed of ILC. Next, a physics-based injection molding model is developed by incorporating the dynamics of the proportion flow valve and the force equation of asymmetric cylinder. The model provides an excellent simulation environment for rapid controller design and tuning. Then the injection molding control strategies are discussed, and a discrete anticipatory learning control plus PI control is applied to ram velocity profile tracking in injection molding process. Last, the time-frequency adaptive filter ILC algorithm application in ram velocity profile tracking is also studied. The ILC approaches are shown to provide a significant increase in system performance. Finally, all the results are summarized and the future works are put forth.
修改评论