| 1. |
- Benvenuti, Luca, et al.
(författare)
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Automotive Control
- 2009. - 1
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Ingår i: Handbook of Hybrid Systems and Control, Theory – Tools – Applications. - 9780521765053 ; , s. 439-470
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Bokkapitel (refereegranskat)abstract
- utomotive systems offer a rich opportunity for hybrid models, controls, and tools. Beyond the traditional use of hybrid models for representing the behavior of the composition of discrete controller and continuous plants, automotive mechanical systems exhibit hybrid behavior as demonstrated in this chapter. In addition, hybrid systems can be used to capture system specifications at the highest level of abstraction and to model implementation architectures thus enabling a rich design space exploration.
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| 2. |
- Arnström, Daniel, et al.
(författare)
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A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive LDLT Updates
- 2022
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9286 .- 1558-2523. ; 67:8, s. 4362-4369
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Tidskriftsartikel (refereegranskat)abstract
- In this technical article, we present a dual active-set solver for quadratic programming that has properties suitable for use in embedded model predictive control applications. In particular, the solver is efficient, can easily be warm started, and is simple to code. Moreover, the exact worst-case computational complexity of the solver can be determined offline and, by using outer proximal-point iterations, ill-conditioned problems can be handled in a robust manner.
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| 3. |
- Arnström, Daniel, et al.
(författare)
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A Linear Programming Method Based on Proximal-Point Iterations With Applications to Multi-Parametric Programming
- 2022
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Ingår i: IEEE Control Systems Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2475-1456. ; 6, s. 2066-2071
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Tidskriftsartikel (refereegranskat)abstract
- We propose a linear programming method that is based on active-set changes and proximal-point iterations. The method solves a sequence of least-distance problems using a warm-started quadratic programming solver that can reuse internal matrix factorizations from the previously solved least-distance problem. We show that the proposed method terminates in a finite number of iterations and that it outperforms state-of-the-art LP solvers in scenarios where an extensive number of small/medium scale LPs need to be solved rapidly, occurring in, for example, multi-parametric programming algorithms. In particular, we show how the proposed method can accelerate operations such as redundancy removal, computation of Chebyshev centers and solving linear feasibility problems.
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| 4. |
- Arnström, Daniel, et al.
(författare)
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Complexity Certification of Proximal-Point Methods for Numerically Stable Quadratic Programming
- 2021
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Ingår i: IEEE Control Systems Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2475-1456. ; 5:4, s. 1381-1386
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Tidskriftsartikel (refereegranskat)abstract
- When solving a quadratic program (QP), one can improve the numerical stability of any QP solver by performing proximal-point outer iterations, resulting in solving a sequence of better conditioned QPs. In this letter we present a method which, for a given multi-parametric quadratic program (mpQP) and any polyhedral set of parameters, determines which sequences of QPs will have to be solved when using outer proximal-point iterations. By knowing this sequence, bounds on the worst-case complexity of the method can be obtained, which is of importance in, for example, real-time model predictive control (MPC) applications. Moreover, we combine the proposed method with previous work on complexity certification for active-set methods to obtain a more detailed certification of the proximal-point methods complexity, namely the total number of inner iterations.
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| 5. |
- Arnström, Daniel, et al.
(författare)
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Complexity Certification of Proximal-Point Methods for Numerically Stable Quadratic Programming
- 2021
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Ingår i: 2021 AMERICAN CONTROL CONFERENCE (ACC). - : IEEE. - 9781665441971 ; , s. 947-952
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Konferensbidrag (refereegranskat)abstract
- When solving a quadratic program (QP), one can improve the numerical stability of any QP solver by performing proximal-point outer iterations, resulting in solving a sequence of better conditioned QPs. In this paper we present a method which, for a given multi-parametric quadratic program (mpQP) and any polyhedral set of parameters, determines which sequences of QPs will have to be solved when using outer proximal-point iterations. By knowing this sequence, bounds on the worst-case complexity of the method can be obtained, which is of importance in, for example, real-time model predictive control (MPC) applications. Moreover, we combine the proposed method with previous work on complexity certification for active-set methods to obtain a more detailed certification of the proximal-point methods complexity, namely the total number of inner iterations.
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| 6. |
- Arnström, Daniel, et al.
(författare)
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Exact Complexity Certification of a Nonnegative Least-Squares Method for Quadratic Programming
- 2020
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Ingår i: IEEE Control Systems Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2475-1456. ; 4:4, s. 1036-1041
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Tidskriftsartikel (refereegranskat)abstract
- In this letter we propose a method to exactly certify the complexity of an active-set method which is based on reformulating strictly convex quadratic programs to nonnegative least-squares problems. The exact complexity of the method is determined by proving the correspondence between the method and a standard primal active-set method for quadratic programming applied to the dual of the quadratic program to be solved. Once this correspondence has been established, a complexity certification method which has already been established for the primal active-set method is used to also certify the complexity of the nonnegative least-squares method. The usefulness of the proposed method is illustrated on a multi-parametric quadratic program originating from model predictive control of an inverted pendulum.
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| 7. |
- Bemporad, Alberto, et al.
(författare)
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Hybrid model predictive control based on wireless sensor feedback : An experimental study
- 2010
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Ingår i: International Journal of Robust and Nonlinear Control. - : Wiley. - 1049-8923 .- 1099-1239. ; 20:2, s. 209-225
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Tidskriftsartikel (refereegranskat)abstract
- Design and experimental validation of model predictive control (MPC) of a hybrid dynamical laboratory process with wireless sensors is presented. The laboratory process consists of four infrared lamps, controlled in pairs by two on/off switches, and of a transport belt, where moving parts equipped with wireless sensors are heated by the lamps. The process, which is motivated by heating processes in the plastic and printing industry, presents interesting hybrid dynamics. By approximating the stationary heat spatial distribution as a piecewise affine function of the position along the belt, the resulting plant model is a hybrid dynamical system. The control architecture is based on the reference governor approach: the process is actuated by a local controller, while a hybrid MPC algorithm running on a remote base station sends optimal belt velocity setpoints and lamp on/off commands over a wireless link, exploiting the sensor information received through the wireless network. A discrete-time hybrid model of the process is used for the hybrid MPC algorithm and for the state estimator. The physical modelling of the process and the hybrid MPC algorithm are presented in detail, together with the hardware and software architectures. The experimental results show that the presented theoretical framework is well suited for control of the new laboratory process, and that the process can be used as a prototype system for evaluating hybrid and networked control strategies.
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| 8. |
- Bemporad, Alberto, et al.
(författare)
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Hybrid Model Predictive Control Based on Wireless Sensor Feedback : an experimental study
- 2007
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Ingår i: Proceedings of the 46<sup>th</sup> IEEE Conference on Decision and Control. - New Orleans, Louisiana, USA. : IEEE. - 9781424414970 ; , s. 5062-5067
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Konferensbidrag (refereegranskat)abstract
- This paper presents the design and the experimental validation of model predictive control (MPC) of a hybrid dynamical process based on measurements collected by a wireless sensor network. The proposed setup is the prototype of an industrial application in which a remote station controls the process via wireless network links. The experimental platform is a laboratory process consisting of four infrared lamps, controlled in pairs by two on/off switches, and of a transport belt, where moving parts equipped with wireless sensors are heated by the lamps. By approximating the stationary heat spatial distribution as a piecewise affine function of the position along the belt, the resulting plant model is a hybrid dynamical system. The control architecture is based on the reference governor approach: the process is actuated by a local controller, while a hybrid MPC algorithm running on a remote base station sends optimal belt velocity set-points and lamp on/off commands over a network link exploiting the information received through the wireless network. A discrete-time hybrid model of the process is used for the hybrid MPC algorithm and for the state estimator.
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| 9. |
- Gros, Sébastien, 1977, et al.
(författare)
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From linear to nonlinear MPC: bridging the gap via the real-time iteration
- 2020
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Ingår i: International Journal of Control. - : Informa UK Limited. - 0020-7179 .- 1366-5820. ; 93:1, s. 62-80
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Tidskriftsartikel (refereegranskat)abstract
- Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to recent progress in algorithms for solving online the underlying structured quadratic programs. In contrast, nonlinear MPC (NMPC) requires the deployment of more elaborate algorithms, which require longer computation times than linear MPC. Nonetheless, computational speeds for NMPC comparable to those of MPC are now regularly reported, provided that the adequate algorithms are used. In this paper, we aim at clarifying the similarities and differences between linear MPC and NMPC. In particular, we focus our analysis on NMPC based on the real-time iteration (RTI) scheme, as this technique has been successfully tested and, in some applications, requires computational times that are only marginally larger than linear MPC. The goal of the paper is to promote the understanding of RTI-based NMPC within the linear MPC community.
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| 10. |
- Herceg, Domagoj, et al.
(författare)
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Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry
- 2017
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Ingår i: 2017 25th Mediterranean Conference on Control and Automation, MED 2017. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509045334 ; , s. 1361-1366
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Konferensbidrag (refereegranskat)abstract
- The steel industry involves energy-intensive processessuch as combustion processes whose accurate modellingvia first principles is both challenging and unlikely to leadto accurate models let alone cast time-varying dynamics anddescribe the inevitable wear and tear. In this paper we addressthe main objective which is the reduction of energy consumptionand emissions along with the enhancement of the autonomy ofthe controlled process by online modelling and uncertaintyawarepredictive control. We propose a risk-sensitive modelselection procedure which makes use of the modern theoryof risk measures and obtain dynamical models using processdata from our experimental setting: a walking beam furnaceat Swerea MEFOS. We use a scenario-based model predictivecontroller to track given temperature references at the threeheating zones of the furnace and we train a classifier whichpredicts possible drops in the excess of Oxygen in each heatingzone below acceptable levels. This information is then used torecalibrate the controller in order to maintain a high qualityof combustion, therefore, higher thermal efficiency and loweremissions
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