Tube-based explicit model predictive output-feedback controller for collective pitching of wind turbines

Lasheen, Ahmed; Saad, M. S.; Emara, Hassan M.; El-Shafei, A.

doi:10.1016/j.renene.2018.07.033

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…to be used in the following formulations. ■ In (20), s is a tuning parameter. It is not straightforward how to solve constrained optimization problem (20) due to its complicated objective function.…”

Section: Demonstrating the Quasi-h ∞ Optimal Tmpc Schemementioning

confidence: 99%

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On the design of efficient optimal tube‐based robust model predictive control: Quasi‐H_∞ approach

Sebghati

Esfahani

Shamaghdari

2023

IET Control Theory & Appl

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This paper proposes a tube‐based robust model predictive control (TMPC) scheme with an optimal tube for disturbance‐affected linear systems. In the literature on TMPC, there is no proper methodology to handle the considerable effects of the tube size on the closed‐loop system performance. There is usually a trade‐off between the disturbance rejection level and the amount of control effort available for the MPC problem. In some applications, it is nearly impossible to find a feasible TMPC to have a sufficient amount of states and inputs feasible sets for the MPC optimization problem. It would be a vital contribution to the TMPC designs if an algorithm is demonstrated which can investigate the suitability of TMPC for a specific system. This paper provides a solution for the mentioned challenges by introducing the concept of Quasi‐H∞ criterion and proposing a constrained optimization problem. The optimization problem is then reformulated and simplified to present an efficient methodology for the TMPC designers. The proposed TMPC scheme could benefit from a larger terminal region and result in a larger region of attraction. The achievements in TMPC designs are shown by simulations and comparisons with the previously used techniquesover numerical case studies.

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Section: Demonstrating the Quasi-h ∞ Optimal Tmpc Schemementioning

confidence: 99%

“…In [17], TMPC has been combined with multi‐stage MPC to benefit from the non‐conservatism of the latter approach. TMPC schemes have been recently applied to various fields of industry including process control ([18] and [19]), wind turbine power control [20], robotics [21], autonomous driving [22] and microgrid energy management [14].…”

Section: Introductionmentioning

confidence: 99%

On the design of efficient optimal tube‐based robust model predictive control: Quasi‐H_∞ approach

Sebghati

Esfahani

Shamaghdari

2023

IET Control Theory & Appl

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“…Automotive Emekli and Güvenç (2016) Pressure control of a diesel engine Vadamalu and Beidl (2016) Energy management of hybrid vehicles Theunissen et al (2019) Suspension systems with road prediction Tavernini et al (2018) Wheel control of an electric vehicle Tavernini et al (2019) Antilock breaking system for breaking Lee and Chang (2019) Autonomous steering control Energy Lasheen et al (2019) Pitch angle control in wind turbines Ogumerem and Pistikopoulos (2019) Temperature control for metal-hydrides Ogumerem and Pistikopoulos (2020) Water electrolysis for hydrogen production Drgoňa et al (2017) Water/methanol distillation column Ziogou et al (2018) Polymer solution of nonlinear mixed-integer multifollower problems (Kassa and Kassa, 2017;Avraamidou and Pistikopoulos, 2018).…”

Section: Area Contribution Descriptionmentioning

confidence: 99%

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

et al. 2021

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The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.

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“…As a result, online optimization is reduced to simple, computationally inexpensive algebraic function evaluations which are valid to regions of optimality and feasibility, referred to as critical regions . Numerous studies on various implementations of mpMPC have been reported in the literature. ,− A major disadvantage of mpMPC is that the computation times to calculate the critical regions offline increase exponentially with the number of variables or the parametric space. , As a consequence, the offline calculations of multiparametric CMPC (mpCMPC) of a process with a large number of variables can get prohibitively expensive. However, for small- and medium-scale systems, the method is well -suited.…”

Section: Introductionmentioning

confidence: 99%

Noncooperative Distributed Model Predictive Control: A Multiparametric Programming Approach

Saini

Pappas

Avraamidou

et al. 2023

Ind. Eng. Chem. Res.

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The distributed control system architecture strikes a balance between the decentralized control system architecture, where subsystem interactions are unaccounted for, and the computationally expensive centralized control system architecture. Subsystem interactions can be significant in chemical process systems, especially when energy or material recycle loops are present. A drawback of the distributed control system is that it is computationally expensive as it requires intermediate iterations involving the solution of multiple optimization problems to be performed. To address this drawback, we develop a noncooperative, iterative, multiparametric distributed model predictive control (mpDiMPC) technique with an aim to decrease the computational costs of conventional, online distributed controllers by avoiding the need to solve an optimization problem at each intermediate iteration. We apply the developed control algorithm on an interacting reactor−separator process and study its control and computational performance. For the case study presented in this paper, mpDiMPC resulted in a reduction in computational costs by approximately 95% compared to its online counterpart.

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Tube-based explicit model predictive output-feedback controller for collective pitching of wind turbines

Cited by 11 publications

References 18 publications

On the design of efficient optimal tube‐based robust model predictive control: Quasi‐H_∞ approach

On the design of efficient optimal tube‐based robust model predictive control: Quasi‐H_∞ approach

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

Noncooperative Distributed Model Predictive Control: A Multiparametric Programming Approach

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Tube-based explicit model predictive output-feedback controller for collective pitching of wind turbines

Cited by 11 publications

References 18 publications

On the design of efficient optimal tube‐based robust model predictive control: Quasi‐H∞ approach

On the design of efficient optimal tube‐based robust model predictive control: Quasi‐H∞ approach

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

Noncooperative Distributed Model Predictive Control: A Multiparametric Programming Approach

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Product

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On the design of efficient optimal tube‐based robust model predictive control: Quasi‐H_∞ approach

On the design of efficient optimal tube‐based robust model predictive control: Quasi‐H_∞ approach