Implementation of Model Predictive Control in Programmable Logic Controllers

Abstract

In this paper we present an implementation of a low memory footprint Model Predictive Control (MPC) based controller in Programmable Logic Controllers (PLC). Automatic code generation of standardized IEC 61131-3 PLC programming languages is used to solve the MPC's optimization problem online. The implementation is designed for its application in a realistic industrial environment, including timing considerations and accounting for the possibility of the PLC not being exclusively dedicated to the MPC controller. We describe the controller architecture and algorithm, show the results of its memory footprint with regards to the problem dimensions and present the results of its implementation to control a hardware in the loop multivariable chemical plant.

Real-time implementation of MPC for tracking in embedded systems: Application to a two-wheeled inverted pendulum

“…A slight offset can be observed for references other than the operating point due to the difference between the prediction model (1) and the real system. This offset could be corrected with the inclusion of a state and disturbance estimator [4]. Once again, the control action reaches its upper and lower bounds during the first moments after each reference change, without having a significant impact on the number of iterations.…”

“…Some examples of these tools being used to implement MPC in embedded systems include [1], [2], [3]. Additionally, other authors propose algorithms that are particularly tailored to the MPC optimization problem, such as in [4], [5], [6], [7]. Finally, another approach is to use explicit MPC [8], which computes the solution of the parametric MPC optimization problem offline and stores it online as a lookup table.…”

“…In [11] the authors presented a sparse solver for the MPCT formulation based on an extension of the classical alternating direction method of multipliers (ADMM) [12] to problems with three separable functions in the objective function [13]. The use of this method resulted in the ingredients of the algorithm having simple structures that could be exploited using a similar approach to the one used in [4], which presented sparse solvers for standard MPC formulations. This lead to a sparse solver with a small iteration complexity and a small memory footprint that was included in the Spcies toolbox [14] for Matlab, which is available at https: //github.com/GepocUS/Spcies.…”

Modular Model Composition for Rapid Implementations of Embedded Economic Model Predictive Control in Microgrids

“…This optimisation of the schedule is repeated periodically with a receding horizon. The implementation of such optimisation methods on embedded hardware for industrial control is a still developing but expanding research field [2][3][4].…”

“…One approach to rapidly implement MPC on embedded hardware is to develop the control solution in desktop software such as Matlab or Modelica and then use automatic code generation for the real-time implementation on the target embedded hardware, as presented by Krupa et al [3]. While providing comfortable high-level engineering tools, this approach also has disadvantages.…”