Move blocked model predictive control with guaranteed stability and improved optimality using linear interpolation of base sequences

Son, Seung Hyun; Park, Byung Jun; Oh, Tae Hoon; Kim, Jong Woo; Lee, Jong Min

doi:10.1080/00207179.2020.1755727

Cited by 13 publications

(6 citation statements)

References 34 publications

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“…A key advantage of utilizing these model identification techniques, based on Koopman operator theory, is the availability of linear predictors for nonlinear systems. This allows the application of well‐established linear MPC schemes 39,40 to develop Koopman‐based model predictive control (KMPC) scheme 41,42 . Furthermore, Abhinav and Kwon 43 proposed a Koopman Lyapunov‐based model predictive control (KLMPC) that integrates EDMD and Lyapunov‐based MPC scheme to ensure the feasibility and stability of a control system, whose mathematically rigorous stability analysis is studied in Ref.…”

Section: Introductionmentioning

confidence: 99%

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

2021

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This work presents the application of a Koopman operator approach to a batch pulp digester. To manufacture paper products with desired properties, it is essential to consider both macroscopic and microscopic attributes of pulp. However, the complexity of multiscale dynamics of pulping processes hinders proper control system design. Therefore, we utilize extended dynamic mode decomposition (EDMD), which is based on Koopman operator theory, to derive a global linear representation of a pulp digester. Then, we design an offset‐free Koopman‐based model predictive control (KMPC) system to regulate the Kappa number and cell wall thickness (CWT) of fibers at a batch pulp digester while compensating for the influence of plant‐model mismatch and disturbance during operation. The numerical experiments demonstrate that the linear state‐space model, obtained via EDMD, properly predicts the behavior of a batch pulp digester, and the designed offset‐free KMPC system successfully drives the Kappa number and CWT to set‐point values.

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Section: Introductionmentioning

confidence: 99%

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

2021

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“…In particular, the authors do not discuss the case in which input move-blocking might be active inside the control invariant terminal set. Son et al [24] extend this embedding strategy by introducing a formulation which additionally interpolates between the previous solution and an LQR base sequence. This extension improves closed-loop performance, especially for LTI systems.…”

Section: Introductionmentioning

confidence: 99%

Suboptimal nonlinear model predictive control with input move-blocking

Makarow¹,

Rösmann²,

Bertram³

2021

Preprint

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This paper deals with the integration of input move-blocking into the framework of suboptimal model predictive control. The blocked input parameterization is explicitly considered as a source of suboptimality. A straightforward integration approach is to hold back a manually generated stabilizing fallback solution in some buffer for the case that the optimizer does not find a better input move-blocked solution. An extended approach superimposes the manually generated stabilizing warm-start by the move-blocked control sequence and enables a stepwise improvement of the control performance. In addition, this contribution provides a detailed review of the literature on input move-blocked model predictive control and combines important results with the findings of suboptimal model predictive control. A numerical example supports the theoretical results and shows the effectiveness of the proposed approach.

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“…The optimal sampling problem is also similar to move blocking (MB) in model predictive control (Son, Park, Oh, Kim, and Lee (2020)). On the basis of a periodic sampling, the MB keeps the input unchanged trough several periods with the aim to reduce the number of variables in the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal sampling pattern for free final time linear quadratic regulator: the scalar case

Balaguer

Alfonso-Gil

2020

International Journal of Control

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The optimal sampling problem is the selection of the optimal sampling instants together with the optimal control actions such that a given cost function is minimized. In this article we solve the optimal sampling problem for free final time linear quadratic regulator with scalar dynamical system. The solution provides the optimal sampling instants, control actions, and the optimal final time in a recursive and constructive way for any arbitrary number of samples N ≥ 1, as it is not based on asymptotic arguments. An application example shows the feasibility of the approach.

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Move blocked model predictive control with guaranteed stability and improved optimality using linear interpolation of base sequences

Cited by 13 publications

References 34 publications

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

Suboptimal nonlinear model predictive control with input move-blocking

Optimal sampling pattern for free final time linear quadratic regulator: the scalar case

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