Abstract
The paper addresses issues associated with implementing GPC controllers in systems with multiple input signals. Depending on the method of identification, the resulting models may be of a high order and when applied to a control/regulation law, may result in numerical errors due to the limitations of representing values in double-precision floating point numbers. This phenomenon is to be avoided, because even if the model is correct, the resulting numerical errors will lead to poor control performance. An effective way to identify, and at the same time eliminate, this unfavorable feature is to reduce the model order. A method of model order reduction is presented in this paper that effectively mitigates these issues. In this paper, the Generalized Predictive Control (GPC) algorithm is presented, followed by a discussion of the conditions that result in high order models. Examples are included where the discussed problem is demonstrated along with the subsequent results after the reduction. The obtained results and formulated conclusions are valuable for industry practitioners who implement a predictive control in industry.
Highlights
Model Predictive Control (MPC) algorithms [1,2,3,4,5,6] are frequently used in industrial applications [7], especially as a technique for efficient control of Multiple-Input Multiple-Output (MIMO) processes
In large-scale industrial applications, MPC algorithms are implemented in Distributed Control Systems (DCS) using Programmable Logic Controllers (PLC) [20] or specialized industrial controllers [21]
When the linear model is used for prediction, a Quadratic Programming (QP) optimization problem results from the control law formulation, which may be solved by readily available solvers
Summary
Model Predictive Control (MPC) algorithms [1,2,3,4,5,6] are frequently used in industrial applications [7], especially as a technique for efficient control of Multiple-Input Multiple-Output (MIMO) processes. In some cases, when the process has many inputs, the resulting internal model may be of high order. This can lead to numerical errors and to instability of the algorithm. Numerical problems appear gradually with increasing model order and occur during the internal model prediction stage of which the results are not directly visible outside of the algorithm. This subject is approached from the perspective of an actual control system project and utilizes the context of the GPC algorithm to illustrate the point. All results presented in this paper comes from simulations performed in MATLAB
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
