How to efficiently implement Model Predictive Control (MPC) in embedded systems is a topic that is attracting a lot of research recently, due to its impact in practical applications. Implementing MPC in industrial Programmable Logic Controllers (PLCs) is of particular interest due to their widespread prevalence in the industry in comparison with other embedded systems, such as FPGAs or microcontrollers. In this paper, we present a PLC implementation of real-time embedded MPC for multivariable systems described by linear time-invariant input/output models subject to upper and lower bounds on input and output variables. The MPC algorithm uses a recently developed primal active-set method for bounded-variable least-squares problems. We highlight and address some crucial challenges that arise in implementing the MPC algorithm in a PLC. Possible extensions of the proposed methods are presented along with hardware-in-the-loop simulation results of controlling a nonlinear multivariable system using a real industrial PLC.

PLC implementation of a real-time embedded MPC algorithm based on linear input/output models

Saraf N.;Bemporad A.
2020

Abstract

How to efficiently implement Model Predictive Control (MPC) in embedded systems is a topic that is attracting a lot of research recently, due to its impact in practical applications. Implementing MPC in industrial Programmable Logic Controllers (PLCs) is of particular interest due to their widespread prevalence in the industry in comparison with other embedded systems, such as FPGAs or microcontrollers. In this paper, we present a PLC implementation of real-time embedded MPC for multivariable systems described by linear time-invariant input/output models subject to upper and lower bounds on input and output variables. The MPC algorithm uses a recently developed primal active-set method for bounded-variable least-squares problems. We highlight and address some crucial challenges that arise in implementing the MPC algorithm in a PLC. Possible extensions of the proposed methods are presented along with hardware-in-the-loop simulation results of controlling a nonlinear multivariable system using a real industrial PLC.
Embedded optimization
Model predictive control
Primal active-set method
Programmable logic controllers
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11771/20226
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