Online model predictive control of industrial processes using low level control hardware: A pilot-scale distillation column case study

Huyck, Bart; Brabanter, Jos De; Moor, Bart De; Impe, Jan Van; Logist, Filip

doi:10.1016/j.conengprac.2014.02.016

Cited by 39 publications

(21 citation statements)

References 26 publications

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“…This issue becomes particularly important when one tries to implement MPC on special purpose hardware, such as FPGA's [21,28], PLC's [32,19,45] or PAC's [18]. For these devices, linear MPC is demanding, as they have limited memory space and very low processing power.…”

Section: Introductionmentioning

confidence: 99%

Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm

Gutiérrez-Urquídez

Valencia‐Palomo

Rodríguez-Elias

et al. 2015

Applied Soft Computing

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Section: Introductionmentioning

confidence: 99%

Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm

Gutiérrez-Urquídez

Valencia‐Palomo

Rodríguez-Elias

et al. 2015

Applied Soft Computing

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“…Such QP solvers are suitable for PC-based implementation, but are unwieldy and exhibit large code and data memory overhead on microcontroller-based embedded platforms; this makes them unsuitable for use unless specialized implementations (e.g., dedicated co-processing hardware [16]) are employed. An alternative for the solution of small QPs that can arise in MPC problems is the use of Hildreth's programming algorithm [17]. This is a row-action algorithm for QPs that has a very simple code implementation, making it suitable for use in industrial devices such as programmable logic controllers (PLCs), programmable automation controllers (PACs), and microcontrollers [17].…”

Section: Previous Workmentioning

confidence: 99%

“…An alternative for the solution of small QPs that can arise in MPC problems is the use of Hildreth's programming algorithm [17]. This is a row-action algorithm for QPs that has a very simple code implementation, making it suitable for use in industrial devices such as programmable logic controllers (PLCs), programmable automation controllers (PACs), and microcontrollers [17]. Although the code and memory overhead is very small, as it is an iterative scheme, convergence to the solution can take an unacceptable amount of time when multiple constraints are active, and even small-sized QPs may push existing hardware to its limitations [17].…”

Section: Previous Workmentioning

confidence: 99%

“…This is a row-action algorithm for QPs that has a very simple code implementation, making it suitable for use in industrial devices such as programmable logic controllers (PLCs), programmable automation controllers (PACs), and microcontrollers [17]. Although the code and memory overhead is very small, as it is an iterative scheme, convergence to the solution can take an unacceptable amount of time when multiple constraints are active, and even small-sized QPs may push existing hardware to its limitations [17]. In [6], an extension to the basic Hildreth algorithm is discussed, which accelerates convergence in practice, but still has linear convergence properties.…”

Section: Previous Workmentioning

confidence: 99%

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A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

Short

Abugchem

2017

Electronics

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Model predictive control (MPC) schemes employ dynamic models of a process within a receding horizon framework to optimize the behavior of a process. Although MPC has many benefits, a significant drawback is the large computational burden, especially in adaptive and constrained situations. In this paper, a computationally efficient self-tuning/adaptive MPC scheme for a simple industrial process plant with rate and amplitude constraints on the plant input is developed. The scheme has been optimized for real-time implementation on small, low-cost embedded processors. It employs a short (2-step) control horizon with an adjustable prediction horizon, automatically tunes the move suppression (regularization) parameter to achieve well-conditioned control, and presents a new technique for generating the reference trajectory that is robust to changes in the process time delay and in the presence of any inverse response. In addition, the need for a full quadratic programming procedure to handle input constraints is avoided by employing a quasi-analytical solution that optimally fathoms the constraints. Preliminary hardware-in-the-loop (HIL) test results indicate that the resulting scheme performs well and has low implementation overhead.

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“…Predictive control is a class of control algorithms in which a dynamic process model is used to predict and optimize system performance. The first predictive control techniques were developed after 1970 driven by the inability of classical controllers to meet the criteria of increasingly demanding performances (Garcia et al, 1989, Huyck et al, 2014.…”

Section: Controlmentioning

confidence: 99%

Smart Monitoring and Decision Making for Regulating Annulus Bottom Hole Pressure While Drilling Oil Wells

Vega

Vieira

Fernandes

et al. 2016

Braz. J. Chem. Eng.

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-Real time measurements and development of sensor technology are research issues associated with robustness and safety during oil well drilling operations, making feasible the diagnosis of problems and the development of a regulatory strategy. The major objective of this paper is to use an experimental plant and also field data, collected from a basin operation, offshore Brazil, for implementing smart monitoring and decision making, in order to assure drilling inside operational window, despite the commonly observed disturbances that produce fluctuations in the well annulus bottom hole pressure. Using real time measurements, the performance of a continuous automated drilling unit is analyzed under a scenario of varying levels of rate of penetration; aiming pressure set point tracking (inside the operational drilling window) and also rejecting kick, a phenomenon that occurs when the annulus bottom hole pressure is inferior to the porous pressure, producing the migration of reservoir fluids into the annulus region. Finally, an empirical model was built, using real experimental data from offshore Brazil basins, enabling diagnosing and regulating a real drilling site by employing classic and advanced control strategies.

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Online model predictive control of industrial processes using low level control hardware: A pilot-scale distillation column case study

Cited by 39 publications

References 26 publications

Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm

Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm

A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

Smart Monitoring and Decision Making for Regulating Annulus Bottom Hole Pressure While Drilling Oil Wells

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