| As a key branch of emerging manufacturing methods,3D printing technology makes up for the high cost and low efficiency of traditional reduced-material manufacturing,and has been widely used in industry,aviation,medical,construction and other fields.Among them,Fused Deposition Modeling(FDM)has become the most common application form in the 3D printing industry due to its simple structure and low price.However,the 3D printer using open-loop control has negative influence factors such as stepper motor lost steps,gap between the transmission belt and gears,etc.,resulting in large trajectory tracking errors during printing,which in turn reduces printing accuracy and production efficiency.Although closed-loop control improves the print quality,it is extremely expensive.Therefore,based on the concept of low cost,this thesis proposes a filter-based closed-loop iterative learning control compensation algorithm,which provides a new method and new idea for the traj ectory error compensation problem of 3D printers.The main work of this thesis includes:According to the mechanical structure and movement characteristics of the 3D printer,and taking into account the non-linear characteristics of the stepper motor and the belt drive itself,a non-linear dynamic model of the 3D printing system is constructed.With the help of linearization,the synchronous belt transmission mechanism is simplified into a spring damping system model,the differential equation expression of the system is deduced and the state space model is established.And the control architecture of the 3D printing system was designed,and the effectiveness of the iterative learning control compensation algorithm was proved through theoretical and simulation analysis.Aiming at the problem of error divergence in the iterative learning control algorithm in practice,a robust Q filter is added to the classic iterative learning control algorithm,the control compensation architecture and algorithm flow design are carried out,and the super vector representation method is used to give the specific Mathematical description.In order to further improve the tracking accuracy of the 3D printing system,from the perspective of the control layer,a Q-filter type closed-loop iterative learning control compensation algorithm for 3D printers is proposed.At the same time,the advantages and characteristics of this algorithm are specifically analyzed through theoretical derivation and numerical simulation.Based on the above algorithm,a control compensation framework for the 3D printer was constructed,and the printer was modified and reconnected to complete the secondary development work.On this basis,a high-precision magnetic scale is selected and installed at the end of the X and Y axes to make the 3D printing experimental table a closed loop system.The dynamic model of the 3D printing experimental table was identified by the least square method,and the repeatability of the controlled system was verified.Finally,through the trajectory error compensation experiments,the trajectory tracking effects of closed-loop control,open-loop iterative learning control and closed-loop iterative learning control feedforward compensation algorithms are compared and analyzed,thereby verifying the effectiveness of the proposed closed-loop iterative learning control feedforward compensation algorithm.The relevant research results have Certain theoretical significance and engineering application value. |
