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Inexact-Uzawa Primal-Dual Solver for embedded model predictive control

IEEE control SYSTEMS LETTERS 2023年 7卷 697-702页

作者： Adegbege, Ambrose A. Harish, Nia Coll New Jersey Dept Elect & Comp Engn Lab Embedded Control & Optimizat Ewing NJ 08628 USA

In this letter, we propose an inexact-Uzawa solver for embedded linear model predictive control (MPC). The inexact-Uzawa algorithm falls into the general framework of first-order primal-dual methods but employs both proximal-point and matrix splitting schemes to derive a numerically robust algorithm with O(1/k) convergence rate in the primal-dual gap to some saddle-point solution where k is the iteration count. Numerical MPC example shows the efficiency and the ease of implementation of the algorithm as compared to other related methods in the literature.

关键词： Inexact Uzawa methods quadratic programming primal-dual iterative methods embedded model predictive control

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Efficiency and performance of embedded model predictive control for active vibration attenuation

Efficiency and performance of embedded model predictive cont...

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European control Conference

作者： Gergely Takács Pablo Zometa Rolf Findeisen Boris Rohal'-Ilkiv Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Nám. slobody 17 812 31 Bratislava Slovakia Institute for Automation Engineering Otto-von-Guericke University Magdeburg Universitatsplatz 2 (G07) 39106 Magdeburg Germany

ISBN: (纸本)9781509025923

The development of efficient solution approaches and technological advances facilitate the use of predictive control on embedded systems, even for fast systems or on computationally limited hardware platforms. The practical implementation of predictive control is, however, still often very time consuming and demands insight into the formulation and solution strategies for predictive control. Tools for automatic code generation tailored for deployment on embedded systems promise to overcome these problems and thus enable the fast and reliable implementation of predictive control. This paper exploits the efficiency and performance of automatic code generation for linear model predictive control for the embedded active vibration control of a flexible mechanical structure. μAO-MPC is used to automatically generate C code, based on a high level problem description. The resulting code is used for real-time implementation on an embedded platform. The embedded model predictive controller efficiently computes the voltage input for a piezoceramic actuator based on state estimates from a Kalman filter. The performance, computational efficiency, memory requirements, task execution timing and other properties of practical interest are examined via experiments on the embedded controller for active vibration control.

关键词： Efficiency performance embedded model predictive control

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Efficient Implementation of Step Response Prediction models for embedded model predictive control

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IFAC-PapersOnLine 2015年第23期48卷 197-204页

作者： Kufoalor, D.K.M. Imsland, L. Johansen, T.A. O.S. Bragstads plass 2D TrondheimN-7491 Norway NTNU Norway

Different formulations of the step response prediction model used in model predictive control (MPC) are examined in this paper. The advantages and disadvantages of the models are uncovered, and a class of model errors that lead to the deterioration of the prediction capability of step response models is identified. Particular attention is paid to the effect of small truncation errors, and the results show that such errors might lead to poor predictions, if the last element in the step response sequence is used to extend the prediction model. Several implementation aspects that are crucial for embedded targets with limited resources are also discussed. The results of the performance and computational enhancements proposed in this paper are analyzed in a simulation study. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： model predictive control embedded systems Errors Forecasting predictive control systems Step response Efficient implementation embedded model predictive control Implementation aspects Prediction capability Response prediction model Simulation studies Step response model Unmatched model

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Scalability of QP solvers for embedded model predictive control Applied to a Subsea Petroleum Production System

Scalability of QP solvers for Embedded Model Predictive Cont...

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IEEE Conference on control and Applications (CCA)

作者： Binder, B. J. T. Kufoalor, D. K. M. Johansen, T. A. Norwegian Univ Sci & Technol Dept Engn Cybernet OS Bragstads Plass 2D NO-7491 Trondheim Norway NTNU Ctr Autonomous Marine Operat & Syst Trondheim Norway

ISBN: (纸本)9781479977871

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.

关键词： embedded systems petroleum industry predictive control programmable controllers quadratic programming FiOrdOs PLC QP solvers computation time cubic growth data memory embedded MPC embedded model predictive control linear growth memory footprint program memory requirements program sizes programmable logic controller qpOASES subsea petroleum production system system configuration system variables Generators Inductors Manifolds Mathematical model Memory management Petroleum Programmable logic controllers Petroleum Industry quadratic programming computational time Data storage devices predictive control Program size Store management inductors Solvers Manifolds embedded systems Petroleum dynamos Mathematical model

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Towards Proper Assessment of QP Algorithms for embedded model predictive control

Towards Proper Assessment of QP Algorithms for Embedded Mode...

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European control Conference (ECC)

作者： Kouzoupis, D. Zanelli, A. Peyrl, H. Ferreau, H. J. Univ Freiburg Inst Microsyst Engn IMTEK D-79110 Freiburg Germany ETH Automat Control Lab CH-8092 Zurich Switzerland ABB Corp Res Segelhofstr 1K CH-5405 Baden Switzerland

ISBN: (纸本)9783952426937

With model predictive control (MPC) becoming a viable approach for advanced feedback control at very fast sampling times, a plethora of methods for solving quadratic programming (QP) problems on embedded computing hardware has been proposed. While most of these methods seem to be useful and superior to competing approaches on particular problem instances, very little effort has been put into a proper benchmarking on a non-trivial number of MPC problems. This paper is intended to help filling this gap by (i) briefly discussing the most important aspects for assessing the suitability of a certain QP method for an MPC problem at hand, (ii) providing a concise overview of about a dozen different QP algorithms that have been proposed for use in MPC, (iii) describing a general benchmarking framework for comparing the numerical performance of the different QP algorithms, and (iv) providing preliminary benchmarking results based on different performance metrics. Numerical performance of the various algorithms is assessed by means of a suitable collection of benchmark problems, taken from both academic studies and industrial applications of MPC.

关键词： control engineering computing embedded systems feedback predictive control quadratic programming MPC QP algorithm advanced feedback control embedded computing hardware embedded model predictive control general benchmarking framework quadratic programming problem Algorithm design and analysis Benchmark testing Hardware Optimization Prediction algorithms Reliability Sparse matrices

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embedded model predictive control on a PLC Using a Primal-Dual First-Order Method for a Subsea Separation Process 22

Embedded Model Predictive Control on a PLC Using a Primal-Du...

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22nd Mediterranean Conference on control and Automation (MED)

作者： Kufoalor, D. K. M. Richter, S. Imsland, L. Johansen, T. A. Morari, M. Eikrem, G. O. Norwegian Univ Sci & Technol Dept Engn Cybernet OS Bragstads Plass 2D N-7491 Trondheim Norway Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland Statoil ASA FD ER SF Rotvoll Norway

ISBN: (纸本)9781479959013

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.

关键词： control engineering computing embedded systems oil technology predictive control process control production engineering computing program compilers programmable controllers quadratic programming separation ABB AC500 PLC FiOrdOs code generator toolbox SEPTIC Statoil estimation and prediction tool for identification and control control community custom ANSI C code generation embedded model predictive control fast QP solver first-order method hardware-in-the-loop simulation light QP solver linear MPC module primal-dual first-order method problem size reduction methods quadratic programming solver subsea compact separation process tailored interior-point method warm-start variant Convergence IP networks Liquids predictive control Real-time systems Separation processes Software

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High-performance embedded model predictive control using Step Response models *

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IFAC-PapersOnLine 2015年第25期48卷 138-143页

作者： D.K.M. Kufoalor L. Imsland T.A. Johansen Dept. ofEng. Cybernetics Norwegian University of Science and Technology (NTNU) O.S. Bragstads plass 2D N-7491 Trondheim Norway Dept. ofEng. Cybernetics and Center for Autonomous Marine Operations and Systems (AMOS) NTNU

This paper presents an efficient approach used to obtain high-performance control targets for an embedded MPC scheme, which has a relatively low computational complexity compared to a field proven industrial MPC framework. The results show that prediction quality is an essential factor to consider when using the embedded MPC scheme for challenging industrial applications where step response models are used. A recursive prediction model implementation and a more straightforward implementation, referred to as the standard implementation are examined in this paper. The results show that both implementations have inherent properties that directly affect control performance in the presence of disturbances. The recursive implementation turns out as the best choice for the embedded MPC due to its superior robustness and computational properties. The impact of the prediction model implementation on the control of a subsea compact separation process is also studied.

关键词： embedded model predictive control step response models robustness to model errors

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An embedded FPGA Architecture for Real-Time model predictive control 21st

An Embedded FPGA Architecture for Real-Time Model Predictive...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Khajanchi, Hussain, I Bruno, Joseph N. Adegbege, Ambrose A. Coll New Jersey Dept Elect & Comp Engn 2000 Pennington Rd Ewing NJ 08628 USA

We develop a custom hardware architecture for real-time implementation of embedded model predictive control (MPC) on a Field Programmable Gate Array (FPGA). We propose a novel modular framework that allows for easy and rapid prototyping of control with capability for analog-to-digital conversion, numerical scaling, and digital-to-analog conversion. We demonstrate the effectiveness of the proposed framework for real-time control on a quadruple water tank system. Copyright (C) 2020 The Authors.

关键词： embedded model predictive control Primal-Dual algorithm Real-Time control Field Programmable Gate Array Quadruple Tank System

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An embedded FPGA Architecture for Real-Time model predictive control

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IFAC-PapersOnLine 2020年第2期53卷 7833-7838页

作者： Hussain I. Khajanchi Joseph N. Bruno Ambrose A. Adegbege Department of Electrical and Computer Engineering The College of New Jersey 2000 Pennington Road Ewing NJ 08628

关键词： embedded model predictive control Primal-Dual algorithm Real-Time control Field Programmable Gate Array Quadruple Tank System

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Towards Parallelizable Sampling–based Nonlinear model predictive control

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IFAC-PapersOnLine 2017年第1期50卷 13176-13181页

作者： Bobiti, R.V. Lazar, M. Department of Electrical Engineering Eindhoven University of Technology Netherlands

This paper proposes a new sampling–based nonlinear model predictive control (MPC) algorithm, with a bound on complexity quadratic in the prediction horizon N and linear in the number of samples. The idea of the proposed algorithm is to use the sequence of predicted inputs from the previous time step as a warm start, and to iteratively update this sequence by changing its elements one by one, starting from the last predicted input and ending with the first predicted input. This strategy, which resembles the dynamic programming principle, allows for parallelization up to a certain level and yields a suboptimal nonlinear MPC algorithm with guaranteed recursive feasibility, stability and improved cost function at every iteration, which is suitable for real–time implementation. The complexity of the algorithm per each time step in the prediction horizon depends only on the horizon, the number of samples and parallel threads, and it is independent of the measured system state. Comparisons with the fmincon nonlinear optimization solver on a benchmark example indicates that, as the simulation time progresses, the proposed algorithm converges rapidly to the “optimal” solution, even when using a small number of samples. © 2017

关键词： Dynamic programming Computational complexity Constrained optimization Cost functions Iterative methods model predictive control Nonlinear programming Nonlinear systems predictive control systems Constrained controls Dynamic programming principle embedded model predictive control Nonlinear model predictive control Nonlinear optimization solver Nonlinear predictive control Number of samples Prediction horizon

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