Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm

This article presents a sparse, low-memory footprint optimization algorithm for the implementation of model predictive control (MPC) for tracking formulation in embedded systems. This MPC formulation has several advantages over standard MPC formulations, such as an increased domain of attraction and guaranteed recursive feasibility even in the event of a sudden reference change. However, this comes at the expense of the addition of a small amount of decision variables to the MPC's optimization problem that complicates the structure of its matrices. We propose a sparse optimization algorithm, based on an extension of the alternating direction method of multipliers, that exploits the structure of this particular MPC formulation. We describe the controller formulation and detail how its structure is exploited by means of the aforementioned optimization algorithm. We show closed-loop simulations comparing the proposed solver against other solvers and approaches from the literature

Desarrollo de un controlador predictivo para autómatas programables basado en la normativa IEC 61131-3

En este trabajo se describe la implementación de un controlador predictivo (MPC) en un autómata programable (PLC) a través de una librería en MATLAB. La librería toma los datos del sistema y los parámetros del MPC y genera con ello el código del controlador de forma que se minimice el uso de memoria del PLC. El código generado se empaqueta en un archivo que puede ser directamente importado al software de control del PLC. El lenguaje de programación de dicho código sigue la normativa IEC 61131-3. En concreto, el controlador está programado con el lenguaje ST. El controlador incluye un observador de estado y un estimador de perturbaciones, un steady state target optimization (SSTO), un predictor en bucle abierto y posibilidad de trabajar en modo manual. Este trabajo proporciona una metodología, basada en el algoritmo FISTA, para resolver el problema de optimización requerido para la implementación del controlador MPC. En este trabajo también se muestran los resultados de pruebas realizadas sobre el uso de memoria del PLC para sistemas de distintos tamaños, así como un ejemplo de uso del controlador aplicado en el PLC Modicon m340, de la empresa Schneider Electric. El software de control del PLC que ha sido usado, y para el que está programada la librería de MATLAB, es Unity Pro XL.MINECO (España) Proyecto DPI2013-48243-C2-2-RFEDER (UE) Proyecto DPI2016-76493-C3-1-

Implementation of Model Predictive Control in Programmable Logic Controllers

In this article, we present an implementation of a low-memory footprint model predictive control (MPC)-based controller in programmable logic controllers (PLCs). 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 regard to the problem dimensions, and present the results of its implementation to control a hardware-in-the-loop multivariable chemical plant.MINERCOFEDER DPI2016-76493-C3-1-

Management of harmonic propagation in a marine vessel by use of optimization

Advances in power electronics drive systems for variable speed operation has enabled extensive use of such solutions in the propulsion and thruster systems of marine vessels. These solutions however introduce current and voltage distortions that compromises the overall power quality of the onboard electrical system. This paper presents and discusses one approach for generating the harmonic current reference for an active filter based on optimization. Two relevant results are revealed by this study: 1) lower THD values are attained by performing system optimization compared to local compensation of one load, and 2) the lower THD values are achieved with a smaller active filter rating than the one required for local load compensation.(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Embedded Model Predictive Control on a PLC Using a Primal-DualFirst-Order Method for a Subsea Separation Process

The results of a PLC implementation of embedded Model Predictive Control (MPC) for an industrial problem are presented in this paper. The embedded MPC developed is based on the linear MPC module in SEPTIC (Statoil Estimation and Prediction Tool for Identification and Control), and it combines custom ANSI C code generation with problem size reduction methods, embedded real-time considerations, and a primal-dual first-order method that provides a fast and light QP solver obtained from the FiOrdOs code generator toolbox. Since the primal-dual first-order method proposed in this paper is new in the control community, an extensive comparison study with other state-of-the-art first-order methods is conducted to underline its potential. The embedded MPC was implemented on the ABB AC500 PLC, and its performance was tested using hardware-in-the-loop simulation of Statoil's newly patented subsea compact separation process. A warm-start variant of the proposed first-order method outperforms a tailored interior-point method by a factor of 4 while occupying 40% less memory