Products > Discipline: Control Systems > Library: PREDICTIVE_CONTROL
PREDICTIVE_CONTROL LIBRARY
The PREDICTIVE_CONTROL library incorporates the multivariate linear predictive controllers DMC and GPC with restrictions including their treatment, feasibility management, reconfiguration of the structure and economical set.
Features
Predictive control algorithms emerged in the middle of the seventies on the part of industrial applications. The main idea was to develop robust control algorithms based on simple, measurable process models, providing acceptable results along with constraints, noises and parameter uncertainties. These algorithms, using predicted future information, could provide better control performance compared to the usual PID control, especially in the case of known reference signals and with a great amount of plant dead time.
According to the receding horizon control strategy only the first control signal is used at the process input, and in the next sampling point the procedure is repeated. Nowadays, it is declared that predictive control algorithms are the second most used algorithms in the process industry – besides PID control. Predictive control algorithms have been developed mainly for linear plants. Predictive control also seems to be a promising technique in the nonlinear environment.
The library implements two linear predictive control algorithms, DMC and GPC, which enables you to select the most appropriate method for the type of process and the control objectives of each application.
Simultaneously with the controller, the tool also implements a set point optimizer which, using the same models, enables you to calculate in line the optimal operation point of the process from an economical point of view which makes the implementation of advanced plant control more attractive.
The implemented software is of a general type so that, when correctly adjusted, it can be used to run in different continuous processes. Before use, the controller has to be configured on the screens where the variables to be used, the models that list them and a set of operation parameters are established.
Components
The library contains two elements:
- DMC Controller (Dynamic Matrix Control): predictive controller with the following advantages:
- Model easy to implement. It can be definitive as a response to a jump or a transfer function
- Attractive for industrial use, because training in its operation is not complicated
- No need for any assumptions concerning the order of the model
- However, it also has some disadvantages; the most important is that unstable processes in open circuits cannot be controlled
- GPC Controller (Generalized Predictive Control): this controller has turned out to be one of the most popular methods in both industrial and academic areas
- In industrial applications, it showed good performance and some kind of solidity with regard to over-parameterization or poorly known delays
- It is effective in non-minimal phase or/and unstable in open-circuit plants
- The model can be defined only as a transfer function