Model Approximation And Controller Tuning For Multi-Volume Processes

With the rapid development of modern society and the increasing complication of industrial processes, the scale of controlled object appeared in the practical application is also growing, which leads to the increasing of the system model order and controller order, and causes the increasing of the computational difficulty and controlling costs. Therefore, model reduction theory and controller design has been a hot research field, although many domestic and foreign researchers have made a lot of contribution in these two field in recent years, they reduce the high-order object which is difficult to be controlled in the actual process. Using a simple low-order model to replace the actual higher-order object can reduce the difficulty of designing a controller and improve the effectiveness and precision of control. However in the basis as well as we can go further to explore the area.On the basis of previous work, this paper does further research on model reduction and controller design contrary to commonly used process in industrial process which is called multi-volume process(MVP). The main contents of the study include the following two aspects: on the model reduction problem, this paper reduce the multi-volume process(MVP) to first order plus dead time(FOPDT) model and second inertia model separately where the order is from two to fifteen, and gives the generic formula. Reduction method is standard particle swarm optimization(PSO). The objective function is the integral of the time weighted absolute error(ITAE). It avoids the complex computing and ensures the stability of the system. On the controller design problem, firstly we get better controller parameters through tuning the reduced model. Then we use the controller parameters to tune the higher-order multi-volume processes, and observe the control quality indicators and the output curve.This paper using the FOPDT model proposed in this paper makes not only the horizontal comparison of controller arguments tuning results for MVP with the other FOPDT models in other documents, but also the longitudinal comparison of controller arguments tuning results for MVP with the second inertia model. The comparative results indicate the FOPDT model proposed in this paper makes a good performance in controller arguments tuning for MVP.