Abstract
Due to the nonlinear dynamics in weighing and feeding process, it is difficult to achieve high accuracy with conventional control methods. This paper uses a piecewise linearization method for the nonlinear problem and discusses the application of iterative learning control in weighing and feeding process. First, the nonlinear problem and the repeatability are discussed based on dynamic analysis of weighing and feeding process. Next, a linear state space model is established with a piecewise linearization method. Then, an iterative learning controller is presented by utilizing repetitive characteristics, and the controller parameters are obtained by using a multi-objective optimization method. Finally, simulation results show that the presented control method improves the control performances and the accuracy of feeding.
Highlights
A weighing and feeding process is a process in which a certain material is weighted and added to a reaction vessel through an actuator, which repeats many times to get more products
A model of a resonant linear electromagnetic vibratory feeder was established based on the kinetic and potential energies, the dissipative function of the mechanical system, and Lagrangian formulation [3]
For the repeatability and the nonlinear problem of weighing and feeding processes, this paper presents an Iterative learning control (ILC) and a piecewise linearization method
Summary
A weighing and feeding process is a process in which a certain material is weighted and added to a reaction vessel through an actuator, which repeats many times to get more products. Regarding the control and modeling of weighing and feeding processes, many results were published for one feeding batch, which considered the vibrating feeder as a linear device. Many good methods were presented to solve nonlinear problems, such as the fixed point index theory in cones which proved the existence of positive solutions of nonlinear boundary value problem [13, 14], the method of upper and lower solutions and different monotone iterative techniques which analyzed a nonlinear third-order differential equation [15], a weighted norm method which analyzed nonlinear Schrodinger equations [16], and using neural networks to approximate the nonlinear function [17, 18] These researches provide good references for the nonlinear problem of weighing and feeding processes.
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