Multivariable predictive control with filtered variables in prediction equations

The paper is focused on an implementation of a multivariable predictive controller with a colouring filter C in a disturbance model. The filter is often essential for practical applications of predictive control based on input-output models. It is commonly considered as a design parameter because it has direct effects on closed loop performance. In this paper a computation of predictions for the case with the colouring filter is introduced. The computation is based on a particular model of the controlled system in the form of matrix fraction which is commonly used for description of a range of multivariable processes. Performance of closed loop system with and without the colouring filter in the disturbance model was compared.Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Programme EEA and Norway Grants [NF-CZ07-ICP-4-345-2016]ERDF, European Regional Development Fund; CZ.1.05/2.1.00/03.0089, ERDF, European Regional Development Fun

A control and monitoring oriented model of a film manufacturing process

This paper describes the development of a control and monitoring oriented model of a plastic film manufacturing process. The model is mainly derived from first-principles and has been implemented in the Matlab/Simulink dynamic simulation environment. The development of the model forms the first phase of a project that aims to develop a nonlinear sub-space based monitoring, fault detection and trouble shooting system for the film manufacturing process

A new multivariable control concept for the falling film evaporator process

The paper presents a new multivariable control concept for falling film evaporators (FFEs). Our concept solves the major challenges encountered in modern FFE control: large transport delays, additional control of the output mass flow, coupling of controlled variables, and disturbances due to time-varying input dry matter content. The challenges are addressed together, for the first time, by the following control design. Based on a dynamic nonlinear input–output model, we consider a linearizing output transformation to enable application of classical linear control methods composed of feedforward design, disturbance rejection, and a decoupling network. Due to these features, we are able to design robust PID and PI controllers that substantially compensate plant-model mismatches. Connecting our concept to a digital twin of the plant yields good performance, which encourages future application of the design in the real-world process

Educational software tool for decoupling control in wind turbines applied to a lab‐scale system

This paper presents an educational software tool, called wtControlGUI, whose main purpose is to show the applicability and performance of different decoupling control strategies in wind turbines. Nowadays wind turbines are a very important field in control engineering. Therefore, from an educational point of view, the tool also aims to improve the learning of multivariable control concepts applied on this control field. In addition, wtControlGUI allows for testing and control of a lab-scale system which emulates the dynamic response of a largescale wind turbine. The designed graphical user interface essentially allows simulation and experimental testing of decoupling networks and other multivariable methodologies, such as robust and decentralized control strategies. The tool is available for master degree students in control engineering. A survey was performed to evaluate the effectiveness of the proposed tool when used in educational related tasks

A classification of techniques for the compensation of time delayed processes. Part 2: Structurally optimised controllers

Following on from Part 1, Part 2 of the paper considers the use of structurally optimised controllers to compensate time delayed processes

Multivariable PID control of an Activated Sludge Wastewater Treatment Process

In general, wastewater treatment plant (WWTP) consists of several stages before it is released to a receiving water body. There are, preliminary and primary treatment (mechanical treatment), a secondary treatment (biological treatment) and a tertiary treatment (chemical treatment). In this chapter, since the work involve of identification and control design of activated sludge process to improve the performance of the system, and most of the control priorities are centred on the biological treatment process, only the secondary treatment will be highlighted

Multivariable model predictive control of a pilot plant using Honeywell profit suite

This thesis documents the first implementation of Profit Suite into Murdoch University’s Pilot Plant. This Pilot Plant is a small scale model of the Bayer Alumina Process. Profit Suite is a Honeywell application that uses Model Predictive Control (MPC) for Multivariable Control (MVC). The major project objective was to connect Profit Suite to the exiting Experion PKS control system then compare multivariable model predictive control to the existing PI control scheme. The project objectives were achieved. Multivariable controllers were built that controlled temperatures and levels in both halves of the plant. The OPC connections between Profit Suite and Experion were completed and documented, as well as the procedures used to build and commission Profit Controllers in the Pilot Plant. Multivariable level controllers were designed using accurate models that performed well. These MVCs performed better than PI control in that they managed all tank levels and recycle streams throughout the plant. Linear objective functions were used to optimize flows and levels with success. Baseline testing of the PI Controllers showed they were better than the MVCs for temperature control. The steam pressure disturbance had no effect on temperatures controlled by fast executing Experion PI controllers. Models found for steam pressure caused MVCs to overcompensate for this temperature disturbance. An MVC built that could manipulate steam valve positions to control temperature performed poorly compared to PI control. Multivariable temperature control was significantly improved when all pumps and steam valves were used as Manipulated Variables by the MVC. Models between water flow rates and temperatures enabled the MVC to use additional pump MVs to counteract the steam pressure disturbance. There was no existing instrumentation to measure steam flowrates from each valve. This required Profit Suite to connect to the OP point of the PI Controllers to directly manipulate valve position for temperature control. Temperature control by cascaded PI steam flow control is recommended to improve the performance of multivariable temperature control. The installation of steam flow transmitters will enable the set point of a PI flow controller to be used as an MV by Profit Control. Fundamental models between steam flowrates and tank temperatures could then be acquired for multivariable control

Model predictive control with exogenous auto-regressive model to improve performance in the CO2 removal

Model predictive control (MPC) is used in the CO2 removal process in the Subang field to improve its control performance. MPC is used to maintain the CO2 concentration at the sweet gas output by controlling the feed gas pressure (PIC-1101), makeup water flow rate (FIC-1102), and amine flow rate (FIC-1103). The empirical model applied to MPC to represent the process model is the auto-regressive exogenous (ARX) model. The ARX model is compared with the first order plus dead time (FOPDT) model based on the root mean square error (RMSE) between the model and the actual process, then MPC parameters are tuned which include sampling time (T), prediction horizon (P) and control horizon (M) to control for the three variables. Improved control performance is measured based on the integral square error (ISE). The results show that the ARX model is the best model for the CO2 removal process with an RMSE value of 35%-91% smaller than the FOPDT model. The optimal control parameters Prediction Horizon (P), Control Horizon (M) and Sampling Time (T) in the CO2 removal process are 75, 25 and 1 on PIC-1101, 25, 10 and 1 on FIC-1102, and 30, 25 and 1 on FIC-1103. The MPC-ARX (MPC using ARX model) can improve the control performance of 33% in the servo control and 6-56% on the regulatory control. However, not all of them showed an increase in control performance improvement from previous studies even though they had used the best model (ARX). This is due to the MPC parameter setting that is not yet appropriate, so it needs to be retuning