Rolling time domain tracking control method for batch injection molding process

Abstract

The invention discloses a rolling time domain tracking control method for a batch injection molding process. The method comprises the steps: building an input and output model by collecting input and output data, selecting an appropriate state variable to build a state space model, further converting the state space model into an expansion state space model containing output and tracking errors, then selecting a performance index containing terminal state, and solving an optimal control law according to a Riccati equation and a boundary condition. Different from a conventional state space model, the novel model of the method takes both a state variable and a tracking error into consideration. On the basis of the newly-designed model, a controller is more flexible to adjust by increasing an adjustable weight coefficient, and then a system can have the better control performance.

Description

technical field [0001] The invention belongs to the technical field of automation and relates to a rolling time-domain tracking control method for a batch injection molding process. Background technique [0002] In actual industrial control, due to factors such as actual working condition drift, process nonlinearity, and system external interference, the control performance of model predictive control systems may decline or even fail after a period of operation. If the controller is not repaired in time to improve the control quality, the economic benefits of the predictive control system will be reduced. At the same time, the actual production process is faced with uncertain factors such as interference, friction, and saturation, which will also cause the mismatch of the controlled object model. Model mismatch is a common problem in predictive control, and it is an important reason for the performance degradation of predictive controllers. As a model-based optimal control...

AI Technical Summary

Problems solved by technology

If the controller is not repaired in time to improve the control quality, it will reduce the economic benefits that the predictive control system can obtain

At the same time, the actual production process is faced with uncertain factors such as interference, friction, saturation, etc., which will also cause the mismatch of the controlled object model

Model mismatch is a ubiquitous problem in predictive control and is an important reason for the performance degradation of predictive controllers

As a model-based optimal control algorithm, if the mismatch between the prediction model of the model predictive control algorithm and the actual object is serious, it will be difficult to improve the performance of the controller only by tuning the controller parameters.

Images