Embedded Model Predictive Control for an Electric Submersible Pump on a Programmable Logic Controller

Binder, Benjamin; Kufoalor, Kwame Minde; Pavlov, Alexey; Johansen, Tor Arne

doi:10.1109/cca.2014.6981402

Cited by 27 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The generated solvers are based on first-order methods, which means that only first-order derivatives (gradients) are used to find the solution. In this paper FiOrdOs is used to generate a custom QP solver based on the primal-dual first-order method proposed in [18], which was also used in [6] and [16]. The QP solver is generated based on the sparse formulation of the MPC problem, cf.…”

Section: A Fiordosmentioning

confidence: 99%

“…Three different QP solvers (FiOrdOs, qpOASES and CVXGEN) were compared in [16], using the two industrial case studies from [5] and [6]. In this paper, the scalability of two of these solvers (FiOrdOs and qpOASES) is studied, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Implementation aspects of MPC on a PLC were discussed in [3] and [4]. Feasibility of embedded MPC on a PLC was investigated in [5] and [6], with realistic case studies from two different applications within subsea petroleum production; an Electric Submersible Pump (ESP) and a subsea separation unit.…”

Section: Introductionmentioning

confidence: 99%

“…The focus in this paper is on the performance of the different QP solvers for varying problem sizes. Control of ESPs using MPC was considered in [17] and [6], where the control problem itself is discussed more extensively.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Scalability of QP solvers for embedded model predictive control applied to a subsea petroleum production system

Binder

Kufoalor

Johansen

2015

2015 IEEE Conference on Control Applications (CCA)

Self Cite

View full text Add to dashboard Cite

The performance of two different Quadratic Programming (QP) solvers for embedded Model Predictive Control (MPC), FiOrdOs and qpOASES, is evaluated for a relevant case study from the petroleum industry. Embedded MPC for the considered system is implemented on a PLC (Programmable Logic Controller) using both solvers. The focus is on the computation time and memory requirements of the solvers as the dimensions of the control problem increase. The results show that qpOASES has a superior performance for small systems with respect to computation time. However, qpOASES has a near cubic growth in computation time with respect to the number of system variables, while FiOrdOs only has a near linear growth. FiOrdOs may thus be faster for larger systems. FiOrdOs has a smaller memory footprint than qpOASES for small systems; however, the program size grows faster than with qpOASES, and for the largest system configuration, the program sizes were almost identical. For even larger systems, qpOASES may have smaller program memory requirements than FiOrdOs, though qpOASES requires more data memory for all problem sizes.

show abstract

Section: A Fiordosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scalability of QP solvers for embedded model predictive control applied to a subsea petroleum production system

Binder

Kufoalor

Johansen

2015

2015 IEEE Conference on Control Applications (CCA)

Self Cite

View full text Add to dashboard Cite

show abstract

“…subsea petroleum production (e.g. [4], [5]), oil well drilling control systems (e.g. [6]), robotic systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

Toward Dependable Embedded Model Predictive Control

Johansen

2017

IEEE Systems Journal

View full text Add to dashboard Cite

Abstract-While Model Predictive Control (MPC) is the industrially preferred method for advanced control in the process industries, it has not found much use in consumer products and safety-critical embedded systems applications in industries such as automotive, aerospace, medical and robotics. The main barriers are implementability and dependability, where important factors are implementation of advanced numerical optimization algorithms on resource-limited embedded computing platforms and the associated complexity of verification. This challenge comes from a requirement of the use of ultra-reliable hardware and software architectures in safety-critical applications, low-cost hardware in consumer products, or both. This paper surveys the state-of-the-art in the emerging field of dependable embedded MPC, and discusses some key challenges related to its design, implementation and verification. A novel result is the study of a simulator-based performance monitoring and control selection method that monitors and predicts MPC performance and switches to a highly reliable backup controller in cases when the MPC experiences performance issues.

show abstract

Addressing the lack of measurements in the subsea environment by using a model scheduling Kalman filter coupled with a robust adaptive MPC

Delou

Souza

Secchi

2020

Braz. J. Chem. Eng.

View full text Add to dashboard Cite

Electric submersible pumps (ESPs) are one of the most widespread oil artificial lifting technologies. In the operation of an ESP there are a large number of parameters that must be monitored and held within operational constraints in order to guarantee stable and optimal operation. Manual control is subject to sub-optimal production and constant violation of operational limits, that can cause either a reduction in ESP lifetime or premature failure. Therefore, a proper automation strategy must be applied to support operators in order to ensure the best production rate with less energy cost. Previous literature has proposed the use of linear MPC based on system identification, however all relevant system variable measurements were considered available. In this paper, the problem of losing measurements of the state variables due to the aggressive subsea environment is addressed. We show that a non-adaptive single linear model strategy lacks in quality for state estimation and, therefore, a robust MPC is not possible under this configuration. In this work, an adaptive constrained MPC coupled with a model scheduling Kalman filter (MSKF) is proposed. Two model scheduling strategies based on linear interpolation of a pre-set number of local models are proposed and compared to successive linearization at every sampling time, based on Taylor series expansion of the nonlinear model. All strategies guarantee model accuracy and model stability over the whole operational range. The proposed scheduling strategies presented similar performance compared to the successive linearization strategy, avoiding the need of obtaining a local linear model at each sampling time.

show abstract

Embedded Model Predictive Control for an Electric Submersible Pump on a Programmable Logic Controller

Cited by 27 publications

References 15 publications

Scalability of QP solvers for embedded model predictive control applied to a subsea petroleum production system

Scalability of QP solvers for embedded model predictive control applied to a subsea petroleum production system

Toward Dependable Embedded Model Predictive Control

Addressing the lack of measurements in the subsea environment by using a model scheduling Kalman filter coupled with a robust adaptive MPC

Contact Info

Product

Resources

About